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The Journal of Neuroscience, May 1, 2001, 21(9):3175-3183
Gating of Human Theta Oscillations by a Working Memory Task
Sridhar
Raghavachari1,
Michael J.
Kahana1, 2,
Daniel S.
Rizzuto1,
Jeremy
B.
Caplan1,
Matthew P.
Kirschen1,
Blaise
Bourgeois2,
Joseph R.
Madsen1, 2, and
John E.
Lisman1
1 Volen Center for Complex Systems, Brandeis
University, Waltham, Massachusetts 02454, and 2 Department
of Surgery, Harvard Medical School and Children's Hospital, Boston,
Massachusetts 02115
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ABSTRACT |
Electrode grids on the cortical surface of epileptic patients
provide a unique opportunity to observe brain activity with high
temporal-spatial resolution and high signal-to-noise ratio during a
cognitive task. Previous work showed that large-amplitude theta
frequency oscillations occurred intermittently during a maze navigation
task, but it was unclear whether theta related to the spatial or
working memory components of the task. To determine whether theta
occurs during a nonspatial task, we made recordings while subjects
performed the Sternberg working memory task. Our results show
event-related theta and reveal a new phenomenon, the cognitive
"gating" of a brain oscillation: at many cortical sites, the
amplitude of theta oscillations increased dramatically at the start of
the trial, continued through all phases of the trial, including the
delay period, and decreased sharply at the end. Gating could be seen in
individual trials and varying the duration of the trial systematically
varied the period of gating. These results suggest that theta
oscillations could have an important role in organizing multi-item
working memory.
Key words:
theta oscillations; working memory; Sternberg; intracranial EEG; brain waves; human
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INTRODUCTION |
Oscillations in the theta frequency
band (4-9 Hz) have been extensively studied in rats
(Vanderwolf, 1969 ; Bland, 1986;
O'Keefe and Recce, 1993; Skaggs et al.,
1996), where they are especially prominent during spatial
exploration. These oscillations can be seen in the field potential and
in the potentials recorded from individual pyramidal cells
(Leung and Yim, 1986; Fox, 1989;
Ylinen et al., 1995; Kamondi et al.,
1998). An important observation that sheds light on the
function of theta is that hippocampal place cells systematically change
their phase of firing relative to theta as the rat moves through a
place field (O'Keefe and Recce, 1993; Skaggs et
al., 1996; Jensen and Lisman, 2000). This
suggests that one function of theta is to provide a reference frame for a neural code in which different spatial information is represented at
different phases of the theta cycle. It remains controversial whether
theta oscillations in the rat are specialized for the organization of
spatial information in the hippocampus or are more generally involved
in other functions (O'Keefe and Burgess, 1999).
Given the importance of theta oscillations in the rat, it has been of
interest to determine whether similar oscillations occur in humans.
Theta band energy can be detected in humans by both MEG and EEG
methods and is evident during working memory tasks (Gevins et
al., 1997; Sarnthein et al., 1998;
Klimesch, 1999; Tesche and Karhu, 2000).
It has recently become possible to observe large-amplitude (>100 µV)
theta oscillations in humans by intracranial EEG (iEEG), a method that
uses electrode arrays to record the EEG directly from the cortical
surface (Kahana et al, 1999a, b; Caplan et al., 2000).
These electrodes are implanted in epileptic patients to determine the
location of seizure foci. The high signal-to-noise ratio of these
recordings makes it possible to detect large-amplitude oscillations
with a clear spectral peak in the theta frequency range and to study
the dynamics of these oscillations during individual trials. This is
not generally possible with the smaller MEG or EEG (1-10 µV) signals
recorded from the scalp. The iEEG study of Kahana et al.
(1999b) showed that theta oscillations occurred in
intermittent bouts during a maze navigation task and that the probability of their occurrence was related to task difficulty. However, it remains unclear whether theta was related to the memory or
spatial components of the task.
To determine whether large-amplitude theta can occur in a task that
lacks a spatial component, we have recorded from intracranial electrode
arrays while subjects performed the Sternberg task, a classic test of
nonspatial, multi-item, verbal working memory (Sternberg,
1966). We found that theta oscillations occur during this task
and have investigated its properties. The Sternberg task is
particularly well suited for examining the temporal properties of theta
because each trial has a well defined period over which working memory
must be maintained. Thus, it was possible to investigate the timing of
changes in theta with respect to the period of working memory.
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MATERIALS AND METHODS |
Subjects
Our four subjects had normal range of personality and
intelligence and were all able to perform the task within normal
limits. Subject 1 (male, age 23), subject 2 (male, age 18), and subject 3 (female, age 22) had implanted electrode arrays, whereas subject 4 (male, age 19) had bilateral depth electrodes in the temporal lobe. The
research protocol was approved by the institutional review board at
Children's Hospital (Boston, MA), and informed consent was obtained
from the subjects.
Intracranial EEG recording
iEEG signal was recorded from arrays (grids or strips)
containing multiple platinum electrodes (3 mm diameter) with an
interelectrode spacing of 1 cm. Grids varied in size but covered
several square centimeters of the cortical surface. The location
of the electrodes was determined using coregistered postoperative
computed tomograms and preoperative MRIs by an indirect stereotactic
technique (Talairach and Tournoux, 1988). The iEEG
signal was amplified, sampled at 200 Hz (Telefactor Corporation
apparatus; band-pass filter: 0.5-100 Hz) for subjects 1 and 2, and at
256 Hz (Biologic Corp. apparatus; bandpass filter, 0.3-70 Hz) for
subjects 3 and 4. Because of clock time discrepancies between the
recording and experimental computers, our clock calibration was
accurate to only ±200 msec.
Sternberg protocol
Lists of 1-4 consonants were presented sequentially on a
computer screen. Although items were presented visually, this form of
the Sternberg task is nevertheless considered a verbal working memory
task because the stimuli are meaningful linguistic units (Baddeley, 1986). To start each trial, a visual
orienting cue was presented 1 sec before the first list item (Fig.
1). Items were presented for 1.2 sec each
with a 200 msec interval between items. The termination of the last
item in the list was followed by a delay period of either 0.9 sec
(subjects 1 and 2) or 2 sec (subjects 3 and 4), after which the probe
was presented. The probe consisted of two letters for subjects 1 and 2 (forced choice variant), with one letter drawn from the presented list.
The subject responded by pressing the left Control key if the first
probe item was on the list and the right Control key if the second
probe item was on the list. Subjects 3 and 4 were tested using the
standard "yes"/"no" version of the Sternberg task, with a
single probe item (Fig. 1a). The subjects responded by
pressing the left Control key if the probe item was on the list and the
right Control key otherwise. After each response, subjects received
accuracy feedback (correct, incorrect) and latency feedback (very fast,
fast, good response time, slow) via a screen message and then initiated
the next trial by pressing a key. The subsequent trial began 1.6 sec
after this key press. The mean interval between the response for one
trial and the start of the next trial was ~2.5 sec. During each
session, trials of each list length were randomly interleaved. We
obtained 50, 96, 140, and 140 trials at each list length for subjects
1-4, respectively. Only correct trials with RTs <2.5 sec were used for analysis. Because there was no significant difference in our results for correct "yes" and "no" trials, data were pooled
across these trial types.
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Figure 1.
Schematic of the Sternberg task illustrating a
four-item list using the "yes/no" procedure. A series of letters
was presented after an orienting cue (+). After a delay period, a probe
item was shown. Subjects indicated whether the probe was on the list,
and RT was measured. After the response, the probe was turned off, and
subjects received feedback on their performance and initiated the next
trial by a key press.
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Exclusion criteria
Subjects were excluded from analysis if their behavioral
performance was poor (mean response times >2 sec or had high error rates). Approximately half the subjects (four of a total of nine subjects) that were tested were able to perform the task
satisfactorily. Sites that were located over known lesions (determined
from clinical records) or were involved in seizure onsets (identified
by examining the seizure records) were excluded as were sites that
showed epileptiform spiking (interictal spikes or spike-and-waves)
activity. A total of 73 such sites (of 320) were rejected.
Data analysis
Power spectra. Because the oscillatory nature of the
iEEG data was of interest, data analysis was done in the frequency
domain. The power spectrum is the Fourier transform of the
autocorrelation function. A simple estimate of the power spectrum, the
magnitude-squared Fourier transform of the data has poor "bias"
(the power at nearby frequencies contribute to the power at any given
frequency, distorting the estimate) and variance (the estimate of the
spectrum does not converge to the true value even if the data length
increases) properties (Thomson, 1982). Multitaper
techniques (Thomson, 1982; Mitra and Pesaran,
1999) provide a formal method to obtain estimates of the
spectrum with optimal bias and variance properties. Briefly, the data
set is windowed (tapered) using a set of special windows (Slepian
windows), which are maximally concentrated in a time duration,
T, and a bandwidth in frequency, W
(Thomson, 1982). The time and frequency resolution of
the windows thus fixes the number of windows, K = 2TW  1, that can be used. The windowed data is then transformed to
the frequency domain by calculating the Discrete Fourier transform,
resulting in K estimates of the spectrum,
Sk(f). Averaging these estimates reduces the
variance of the spectrum by  . Our typical choices for T and W were 1 sec and 2 Hz, respectively.
The averaged power spectra were obtained by averaging the single trial estimates.
Spectrograms. The spectral properties of stationary data
sets do not change over time, i.e., the power spectrum of any stretch of data is statistically similar to any other stretch. If however, the
spectrum varies over time, the data set is nonstationary. One method to
quantify nonstationarity is to compute a time-varying spectrum, or
spectrogram. Spectrograms were computed using the squared modulus of
the complex demodulates [projection of the iEEG data onto different
frequency bands using filters (1 sec duration, 4 Hz bandwidth)
constructed from the Slepian windows (Mitra and Pesaran,
1999)]. Estimates from different Slepian windows were averaged
together to obtain the spectrogram for each trial. The spectrograms for
each trial were aligned with the onset of the first list item and
averaged together. Only oscillatory activity with high signal-to-noise
ratio will be apparent in averaged spectrograms (Tallon-Baudry
et al., 1996).
Test for gating. Gating of theta was tested by comparing the
energy in the average spectrogram during the trial to the energy in the
1 sec before the orienting stimulus. Because the distribution of
energies in the spectrogram is non-Gaussian, a nonparametric method
(Mann-Whitney U test; p < 0.05) was used
to compare the average energy in each 250 msec epoch during the trial
with the intertrial energy. Because the analysis windows were
1-sec-long, adjacent 250 msec bins are not independent. Multiple
comparisons (for the number of electrodes, frequencies and bins) were
corrected for by a Bonferroni correction.
Nonstationarity test. A second method to quantify
nonstationarity is to expand the spectrogram, S(f, t),
along an orthogonal set of basis functions
Al(t) such that:
results in coefficients, al(f)
that are functions of the frequency alone, with L denoting
the number of terms retained in the expansion. Quadratic-inverse theory
(Thomson, 1990, 2001) can be used to pick an appropriate basis set,
Al(t), such that the number of terms
in the expansion, L is fixed to be 4TW, where T and W are defined above, fixing the time and
frequency resolution. Coefficients of higher order are identically
zero. The coefficients, al(f), of for
the quadratic-inverse basis then take on special meaning. The 0th order
coefficient, a0(f) is approximately
S(f), or the time-averaged spectrum. The first order
coefficient, a1(f), is the time-derivative of
the spectrum and so on. Thus, features such as sharp or gradual changes
in power, frequency drifts etc. can be readily identified in a noisy background.
For a constant amplitude signal of a single frequency, the expansion
coefficients vanish for all orders  1. For a stationary process, the
ratio:
where S(f) is the mean power, is
  -distributed. If the signal is systematically
nonstationary at a given frequency f across several trials,
the ratio will be significantly different from the expected value
L 1 for a process. This
results in a single number representing the amount of nonstationarity
at each frequency. Consider a stretch of iEEG data around a trial of
the Sternberg task. If the spectral characteristics of the iEEG change
because of the onset and offset of the task, or within the task itself,
the nonstationary index  (f), will be significantly
different from that expected by chance. The degrees of freedom,
L for a single trial is equal to the highest order term
retained in the expansion, Lmax. For multiple
trials, this becomes Lmax × Ntrials. We considered the signal to be
nonstationary at a given frequency f if the ratio exceeded a
percentile threshold (typically 99.999% or p < 0.00001) of the distribution at that
frequency. It is appropriate in our case to use a high value of
significance given the large number of frequencies (256) and sites that
were tested. This test allows a classification of iEEG data as
stationary or non-stationary at a given frequency.
Test for continuity. To assess the continuity of theta
within a trial, the baseline level of theta was first established by calculating the spectrum for a 1 sec interval after the response for
each trial, and the individual estimates of the spectrum were log-transformed. The jack-knife variance (Mitra and Pesaran,
1999) was computed from individual estimates by leaving out one
trial at a time and averaging over the remaining estimates. The
resulting jack-knife statistic is t-distributed, and a
threshold value was chosen as the 99.999% point of this distribution.
The continuity of theta within a trial was now assessed as the fraction
of trials (from all trials of all list lengths) at which the
narrow-band power dipped below the threshold for any interval of >0.25
sec. The jack-knife statistic is used because it is a robust measure that does not make any assumptions about the underlying distribution of
the data.
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RESULTS |
Figure 1 illustrates the structure of each trial of the Sternberg
task. We visually presented lists of one to four consonants. After a
delay period, the subjects' task was to indicate as rapidly as
possible whether a probe item was on the list. We quantified the speed
of the response by measuring the response time (RT). This task was
administered to three subjects who had intracranial electrode arrays
and one with depth electrodes. Each of these subjects performed the
Sternberg task with very high accuracy; for subjects 1-4, accuracy was
86, 98, 97, and 96%, respectively. RT increased significantly with
list length (LL) for all subjects (p < 0.005).
This increase, approximated by the equation RT = a × LL + b msec, had coefficients (a, b) = (89, 817),
(95, 1008), (40, 463), and (37, 353) for subjects 1-4,
respectively. The differences in RTs between the first two and last two
subjects was most likely a consequence of differences in the design of
the trial structure (see Materials and Methods).
To examine the oscillations occurred during the Sternberg task, it was
desirable to have an unbiased algorithm to detect consistent task-related changes in several frequency ranges. Because the data set
obtained was extensive (~200 trials/subject; total of 247 sites),
examination of the entire data set by eye was impossible. We adapted a
test developed by Thomson (2001) to detect task-related changes in different frequency bands (see Materials and Methods). The
nonstationarity index, (f), identifies electrodes in
which the task produces transient or maintained changes in spectral power at any frequency f. No assumptions are made about the
timing, duration, or sign of the changes. The only requirement for
detection is that the changes be consistent across trials.
This nonstationarity test was applied to all sites that were not
rejected for epileptic artifacts (see Materials and Methods). Figure
2, a and b, shows
(f) for two representative electrode locations. The
broken line in the two panels represents the 99.999% confidence level
for the statistic. Figure 2a shows an electrode for which
(f) exceeds this level in the theta, beta, and gamma frequency bands, indicating consistent task-related changes at these
frequencies. Figure 2b shows an electrode in which
(f) did not exceed the required significance level at any
frequency, suggesting little or no task-related activity at this site.
We detected a total of 74 electrodes (of a total of 247 across all subjects) for which (f) exceeded the 99.999%
significance level at one or more frequencies. The electrode locations
of these sites were widely dispersed over the cortex (24 in the
temporal lobe, 18 in the occipital lobe, 18 in the
parietal/motor/premotor areas, and 14 in the frontal lobe). Figure
2c shows a plot of the number of nonstationary electrodes at
different frequencies. The majority of these (60) had significant
nonstationarity in the theta frequency range (4-9 Hz), most
prominently between 6 and 8 Hz. We therefore conclude that there are
widespread task-related changes in theta during a memory task that
lacks a spatial component. Task-related changes were also observed in
the gamma frequency range, but these will be analyzed elsewhere.
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Figure 2.
iEEG data show significant task-related
nonstationarity, predominantly in the theta frequency band. To be
considered significant, the nonstationarity index must be higher than
the horizontal line, which denotes the 99.999% point of the
2 distribution with the appropriate degrees of freedom
(see Materials and Methods). Nonstationarity index (f)
shown for two representative electrodes. a, Subject 1, Talairach coordinates (left-right, anterior-posterior,
inferior-superior) are +44, +11, +38. This electrode exhibits
significant nonstationarity in the theta and beta bands, as well as
some peaks in the 20-30 Hz range. b, Subject 3, Talairach
coordinates are 42, +8, +42. This shows an electrode that has no
significant peaks. In both cases, the average power spectrum had peaks
in the theta frequency range. For a majority of frequencies, the
measure is distributed between the 5 and 95% confidence intervals with
most values around the theoretical mean value, indicating that the
method is appropriate. c, Summary plot of the number of
electrodes which showed significant nonstationarity as a function of
frequency. A given electrode could show nonstationarity at several
different frequencies.
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To determine how theta changed during the task, we computed
trial-averaged spectrograms for the nonstationary sites. An examination of these spectrograms revealed an interesting pattern of task-related activity at some sites: theta power increased at the beginning of the
trial, was elevated through item presentation and the delay period, and
decreased after the response. Figure
3a shows the averaged
spectrograms from sites that display such a pattern in each of the four
subjects. The average spectrograms for these sites show a clear peak in
the 4-9 Hz range, the theta frequency band. Although peak frequencies
and overall levels of activity varied across subjects, the general
pattern at these gated sites is similar. Because the pseudocolor plots
of the spectrograms emphasize certain transitions in power while making
others less visible, it is important to graphically plot changes in
theta power as a function of time. Figure 3b shows the
evolution of narrow-band power at the same four sites averaged over all
trials with two-item lists (4 Hz bandwidth around the peak frequency in
the spectrogram). In all cases, theta power was elevated during the
trial relative to the intertrial period. Note that the falling phase
before trial onset in the bottom left panel occurs because the average
intertrial interval for this subject was unusually brief (2 sec):
because this interval was of the same order as the data window for the
spectrogram (see Materials and Methods), the falling phase can be
attributed to the offset of the previous trial. Note also that the
shifts in theta power during the trial were relatively small. The most
prominent feature at these sites is the gating "on" of theta at the
onset of the trial and the gating "off" at the end of the trial.
The average changes in power were high, increasing by a factor of 2 (top panels, subjects 1 and 2) or 8 (bottom panels, subjects 3 and 4).
One question that remains unclear is whether theta is activated by the
cue initiating the trial or the presentation of the first memory item.
Technical limitations (see Materials and Methods) prevent us from
determining the onset of gating with a precision better than ±200
msec. It is therefore unclear whether theta turns on with the orienting cue in anticipation of the need for engaging working memory or whether
it turns on with the presentation of the first memory item. Experiments
with longer delays between the orienting cue and the first list item
would be useful in clarifying this issue.
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Figure 3.
Theta is gated during the Sternberg task.
a, Time-frequency energy averaged over all two-item lists
shows sustained theta activity. An example is shown from each of the
four subjects. The data illustrated were obtained from a right frontal
site in subject 1 (Talairach coordinates are +44, +11, +38)
(top left); a left temporal site in subject 2 (Talairach
coordinates are 42, 9, 10) (top right); a right
frontal site (Talairach coordinates are 52, 34, +38) in subject 3 (bottom left); and a depth electrode in left temporal lobe
(Talairach coordinates are 25, 72, +6) in subject 4 (bottom
right). Black bars in the spectrograms denote the trial
duration from the orienting cue to the mean response time for two item
lists. Because of limitations in our synchronization techniques and
methods of time-frequency analysis, the determination of the onset and
offset of theta has a precision of ±200 msec. The color scale
represents power in square microvolts. In subject 1, we also
observed similar gating centered around 18 Hz. However, because this
finding was not duplicated across subjects, we did not analyze it
further. b, Evolution of average theta power in time for the
above four electrodes for a bandwidth of 4 Hz around the peak
frequency. The dot-dashed vertical line marks the orienting
cue, the two solid lines denote the list items, and the
dashed line denotes the probe. Theta power is elevated
throughout the trial, with fluctuations within the trial. The error
bars denote the 95% confidence intervals.
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It was desirable to develop a statistical test to determine whether
this gating was statistically significant and whether it could be seen
at a large number of sites. We therefore adopted a test for gating:
that the average theta power (across trials) in every overlapping 250 msec epoch within the trials exceed the power during the intertrial
period at the 95% confidence level. Thirty sites (of the 74 classified
as nonstationary) met this criterion (p < 0.01, by a Bonferroni corrected, two-tailed, Mann-Whitney U
test). One or more gated sites was detected in each of our subjects. It
should be emphasized that sites that pass the gating test necessarily have an increase in theta power during the "pure" memory period, i.e., the interval after the offset of the last list-item and the onset
of the probe (0.9 sec in the forced-choice variant and 2 sec otherwise)
compared with the baseline power immediately after the response and
before the onset of the subsequent trial (t test;
p < 0.01). This observation indicates that theta is
engaged during the pure working memory period without possible
confounds of item presentation. The remainder of the electrodes that
showed significant nonstationarity in the theta range typically had
elevated theta power during a fraction of the trial duration, and thus were not classified as gated sites. We will not discuss these sites further.
To determine whether gating was dependent on the duration of the task,
we examined responses to trials of different list lengths (and
consequently trial duration). Figure 4
illustrates the change in gating with list length. Two examples are
shown, one from a recording site on the surface of the left parietal
lobe and one from a depth electrode in the left temporal lobe. In both
cases, the duration of sustained theta closely followed the duration of
the trial. It can also be seen that the maximum of the average theta
power at these sites did not vary significantly with list length.
Similarly, the frequency of theta did not change as additional items
were presented (Fig. 3a). The pattern of gating at other sites was similar. We conclude that theta oscillations of relatively stereotyped frequency and power were gated by each trial of the task
and that the period of gating coincided well with the duration of the
trial.
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Figure 4.
Gating varies systematically with list length.
Averaged theta power (5-9 Hz) as a function of time shows that theta
is elevated for the entire duration of the trial. The three different
traces are averages over trials with two-, three- and four-item lists
(circles, squares, and diamonds,
respectively). Gray bars mark the presentation of the list
items, and the black bars mark the delay interval until the
presentation of the probe for the two-, three-, and four-item lists.
The large tick at 1 sec marks the onset of the orienting cue.
a, Recording from a subdural electrode in the parietal
cortex (subject 3, Talairach coordinates are 52, 34, +38).
b, Recording from a depth electrode in left temporal lobe
(subject 4, Talairach coordinates are 25, 72, +6). The rise
subsequent to the end of the trial is attributable to the onset of the
next trial.
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Although Figures 3 and 4 indicate that the average theta power is
continuous at gated sites, the possibility remains that theta is not
continuous during individual trials. In fact, this seemed likely,
because previous iEEG recordings (Kahana et al., 1999b) showed that theta occurs intermittently during a
spatial maze navigation task. As seen in Fig.
5a, which shows an unfiltered trace, theta appears to be continuously elevated during a trial of the
Sternberg task. Indeed, theta oscillations were similarly gated during
each of five consecutive trials (Fig. 5b). Also shown (Fig.
5c) is the time evolution of the narrow band power (2 Hz bandwidth) at the peak frequency (7 Hz) over the course of these successive trials. This plot shows that theta power during the task was
greater than the level during the intertrial periods for a large
fraction of each trial. In a more rigorous analysis of the ten gated
sites with the largest amplitude theta (central region in subject 3;
depth electrodes in subject 4), we calculated the fraction of trials
for which there was a return to baseline theta power (see Materials and
Methods) for any interval >0.25 sec during individual trials. The
fraction of such trials was very low (ranging from 0.05 to 0.1 over all
trials for all three list lengths). We conclude that there are many
sites at which theta is continuous or nearly so during individual
trials.
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Figure 5.
Gating of theta oscillations is evident in single
trials of the raw iEEG signal. a, Sample raw iEEG trace
recorded from an electrode in the parietal cortex (subject 3, Talairach
coordinates are 52, 34, +38) during a two-item list. The
black bar below the trace marks the task duration, whereas
the ticks denote the presentation of the list items, probe,
and response, respectively. b, A 50 sec iEEG trace with five
consecutive trials from the same electrode shows clear enhancement in
theta activity for the duration of each trial. Bars and
tick marks are as above. c, Narrow-band power
(7 ± 1 Hz) for the 50 sec trace above shows clear enhancement
during trials relative to intertrial intervals.
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Several interleaved controls indicate that the signals at theta-gated
sites were not directly related to sensory stimulation or to the
execution of a motor response. Between successive trials, subjects were
given visual feedback regarding their performance on the previous
trial. This information was presented on the same monitor as the list
items. However, as illustrated in Figure
6, this sensory stimulus did not evoke
theta activity at gated sites. A second issue concerns the possibility
that theta might occur in preparation for motor responses. However,
Figure 6 shows that theta did not occur in anticipation of the motor
response (key press) by which subjects initiated the next trial. More
quantitatively, we compared the theta power in the 1 sec before the
response at the end of the trial to the theta power 1 sec before the
key press (75 trials of all list lengths in each subject with a 1 sec
interval between the response and the key press). The theta power
before the key press was significantly smaller
(p < 0.01; t test) than before the
response. We conclude that theta activation cannot be simply explained
as a sensory or motor preparatory response.
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Figure 6.
Theta activity not caused by sensory stimulus or
motor responses. Averaged spectrogram for a site showing gated theta
activity (subject 1, Talairach coordinates are +44, +11, +38). The
trials were aligned to the response. The bars marked
Feedback and Key denote the sensory stimulus
(visual feedback after the response) and the mean time of the motor
response to initiate the subsequent trial, respectively. One hundred
trials of all list lengths with a mean delay of 1 sec between the
response and the key press (to initiate the next trial) were used to
compute the spectrogram. Theta activity (~6 Hz peak frequency) has a
sharp offset after the response and stays off until the beginning of
the next trial (1.6 sec after the key press). The small increase in
theta activity after the key press is caused by averaging trials of
different mean intervals between the feedback and key press. The color
scale represents power in square microvolts per Hertz.
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Although we have found a large number of gated sites (30), it is
difficult to make any strong conclusions about the distribution of
these sites on the cortical surface because of the sparse sampling. It
is important to understand that the electrode arrays were placed in
candidate seizure loci. While providing details within the coverage
area, the electrodes only covered a small fraction of any one lobe.
Thus, our failure to detect activity in a given lobe of a subject does
not imply that it was not present in that lobe. Despite these
limitations, it is important to document the location of gated sites
(Fig. 7). These appear to be distributed widely over the cortex in frontal, temporal, parietal, and occipital lobes. Furthermore, in two patients in which theta was detected with
large arrays, we found that many of the gated sites were clustered near
each other. However, closely spaced sites did not necessarily show
similar activity. Figure 8 shows average
spectrograms (left panels) and average power spectra
(right panels) from each of three nearby electrodes (1-2 cm
separation; subject 3). Sites with gated theta activity (middle
row) sometimes occurred near other sites with no clear
task-related theta activity (top row). Furthermore, in this
subject (subject 3), there were sites (bottom row in Fig. 8)
where theta activity was gated off by the task (i.e., the theta power
was suppressed throughout a trial and rebounded after the response).
Because such "off" gating was only detected in one subject, we
describe it here only because it provides further evidence that
large-amplitude theta can be very different at closely spaced
sites.
View larger version (22K):
[in this window]
[in a new window]
|
Figure 7.
Views of standardized brain showing electrode
locations of gated sites. Filled symbols indicate electrodes
in which theta was gated "on" by the task. Open symbols
indicate electrodes that did not meet our significance threshold.
Different symbols indicate different subjects.
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View larger version (57K):
[in this window]
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|
Figure 8.
Nearby regions (spacing ~1-2 cm) can have
dramatically different patterns of theta activity. Left
panels show averaged spectrograms. Right panels show
averaged power spectra for the corresponding electrodes (solid
lines denote in-task power, dot-dashed lines denote
out-of-task). The white bar in the top left panel
marks the duration of the trial from the onset of the orienting cue
until the presentation of the response. All three electrodes show theta
activity evidenced by the peaks in the power spectra. The top
panel shows an electrode with continuous, theta activity that is
weakly modulated by the task. Significant task-gated theta increase in
activity is evident for the electrode in the middle panel.
The bottom panels show suppression of theta activity during
the trial. (Talairach coordinates from top to
bottom are 50, 14, +41; 54, 16, +29; 56, 24,
+23.)
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DISCUSSION |
Theta in rats has been most reliably elicited by movement, and it
has therefore been suspected that theta may have a special role in
spatial processing. It was thus of considerable interest that the first
observation of large-amplitude theta in humans was during a spatial
task (Kahana et al., 1999b). However, this task
also had a memory component, leaving the possibility that theta might
also occur in memory tasks that lack a spatial component. We therefore
obtained iEEG data from subjects performing a verbal working memory
task to test whether this nonspatial, working memory task also elicited
large theta frequency oscillations. Using an objective test for
nonstationarity, we showed that the Sternberg task evokes clear
task-related changes in the iEEG in the theta frequency band (Fig. 2)
at some cortical and subcortical sites. The power spectra showed a
theta peak, the amplitude of which increased markedly during the task
compared with baseline levels. Our finding that theta occurred during a
task that lacked a spatial component strongly argues against the view
that human theta is uniquely specialized for spatial computations. This
conclusion is consistent with several observations in rat
(Macrides et al., 1982; Givens and Olton
1990, 1995;
Givens, 1996) and humans (Gevins et al.,
1997; Sarnthein et al., 1998; Klimesch,
1999; Tesche and Karhu, 2000), indicating that
theta can occur in nonspatial contexts.
A second major finding of this study is that we detected a
large number of sites (30 in four subjects) in which the amplitude of
theta oscillations increased at the beginning of the trial, stayed
elevated through the entire trial, and decreased at the end (Fig. 3).
We term this phenomenon "gating." When the duration of the trial
was changed, theta gating changed accordingly (Fig. 4). Although
previous EEG studies indicated that theta could occur during working
memory tasks (Gevins et al., 1997; Klimesch,
1999), the timing of the involvement of theta was not
investigated because the structure of the tasks was not suited for the
study of timing issues. In contrast, the working memory component of
the Sternberg task has a well defined onset and offset, which allowed
us to detect a direct correlation of theta with task duration.
Although average theta power (across trials) was gated at
these sites, we confirmed that individual trials also exhibited this
gating. The high signal-to-noise ratio of the iEEG allowed us to
determine that theta is continuous, relative to a baseline, at
electrodes with high-amplitude, gated theta (Fig. 5). This is in
contrast to the intermittent nature of theta observed in a spatial task
(Kahana et al., 1999b). However, it is possible that if we knew when the memory demands occurred in the latter task, it
would be continuous during those periods. The continuous nature of
theta has important implications for models of working memory that are
based on oscillatory activity (see below).
Several findings indicate that activity observed at
theta gated sites cannot be a simple consequence of the sensory and
motor components of the task. In interleaved controls (Fig. 6), we
found that a sensory input or a motor response that was unrelated to working memory did not evoke theta at gated sites. These results, along
with the tight temporal linkage of theta gating to the onset and offset
of the period of working memory, suggest that theta oscillations may
play an important role in human verbal working memory.
Spatial organization of theta
Our findings indicate that gated theta, although common, is not
uniformly present (Fig. 7). The locations of gated sites were widely
dispersed over the cortex. Although iEEG is uniquely well suited to
give a fine grained, high temporal resolution view of theta, it is not
well suited to establishing the regional localization of theta, because
the grid placement is sparse and determined solely by clinical
considerations. It is nevertheless tempting to try to relate the
limited data available to brain regions implicated in working memory by
fMRI methods (Ungerleider, 1995; Goldman-Rakic, 1995; Smith and Jonides, 1998). However, we
caution against this for several reasons. First, recent work on theta
in rats indicates that periods of high and low theta have nearly the
same overall rates of firing (Csicsvari et al., 1999).
Thus, changes in theta amplitude may not be detected by hemodynamic
methods. A second point concerns the special methods that are used in
fMRI studies to isolate the brain regions that are specifically
involved in an aspect of brain function by subtracting the activation
evoked by a simpler task that controls for sensory and other processes. In our case, all areas engaged by the task, including purely sensory areas, might be expected to show task-related oscillations. Indeed, the
activation we observe in the occipital cortex might be related to
sensory processes rather than memory processes. Our finding that widely
distributed brain regions generate theta during a working memory task
is consistent with EEG studies also show increased theta
synchronization between posterior and frontal regions (Sarnthein et al., 1998) during a working memory task.
Oscillatory basis of working memory
Electrophysiological studies of working memory indicate that
persistent firing of cells underlies working memory
(Goldman-Rakic, 1995). Our results suggest that this
firing may have an oscillatory character. Oscillatory single unit
activity has not generally been reported in the delayed response tasks
in monkeys (but see Nakamura et al., 1992), but it is
not clear how to relate animal electrophysiological studies on
single-item nonverbal working memory (Goldman-Rakic,
1995) to the multi-item verbal working memory that we have
studied in humans. It is possible that verbal working memory is more
complex than the simpler forms used in animal studies (Baddeley,
1986) and that this may explain why oscillatory activity has
not generally been seen in single units during simple working memory
tasks in monkeys.
Relevance to models
Memory performance in the Sternberg task has been extensively
studied, and the behavioral results strongly constrain possible models.
Jensen and Lisman (1998) have proposed several variants of oscillatory models that account for the details of response time
distributions in the Sternberg task. Their models were inspired by the
observation (in rat) that different spatial information is encoded at
different phases of the theta cycle (see introductory remarks). They
propose that similar phase coding may be important in multi-item
working memory (Lisman and Idiart, 1995; Jensen and Lisman, 1998) with different memory items active at
different phases of the theta cycle. The continuous nature of theta
during individual trials of the Sternberg task (Fig. 6) provides
support for such models. In one of the variants of the Jensen-Lisman
model, the frequency of theta oscillations was assumed to decrease as a
function of the number of items being held in working memory. This
model would seem to be ruled out by our finding that theta frequency
does not vary significantly with memory load (Fig. 3a) at
gated sites. A second model was based on the assumption that the phase
of theta is reset by the arrival of the probe, an assumption that is
supported by recent MEG results (Tesche and Karhu,
2000).
Although we have focused here on the possible role of theta in
multi-item working memory, there are other possible roles, none of
which are mutually exclusive. One possibility, for which studies of
long-term potentiation provide some evidence (Pavlides et al.,
1988; Huerta and Lisman, 1993), is that theta is
used to rapidly encode information directly into long-term memory by synaptic modification. Another possible function of theta is to synchronize different regions of the cortex that participate in the
task (Sarnthein et al., 1998). Analysis of the
synchronization of theta at different sites during the Sternberg task
is currently underway.
Concerns about the validity of data derived from
epileptic patients
Intracranial recordings from epileptic patients are increasingly
being used to study brain activity during cognitive tasks (Fried
et al., 1997; Fernandez et al., 1999;
Kahana et al., 1999b; Caplan et al.,
2000; Kreiman et al., 2000). In such studies the possible contribution of epilepsy to the conclusions needs to be
addressed. A number of observations suggest that the presence of theta
activity during the Sternberg task is not a result of seizure activity.
First, the precision of the gating (Fig. 3) and the high degree of
spatial localization (Fig. 7) are exactly the opposite of what would be
expected from an uncontrolled process like epilepsy. Second, because
the location of the seizure origin is not known before the electrode
implantation, iEEG from many regions is sampled to identify the focus.
Thus, many of the sampled sites are far from the clinically determined
epileptogenic foci. We observed task-related theta in each of our
subjects at sites that were distant from the seizure foci, sometimes
even in different hemispheres (in subjects 1 and 4). Third, direct
examination of seizure activity in these subjects showed it to have a
much higher amplitude (>1 mV peak-to-peak) with different spectral
structure than even the largest amplitude theta signal during the task
(200 µV peak-to-peak). Fourth, recent work using MEG has detected
theta activity in normal subjects during the Sternberg task
(Tesche and Karhu, 2000). Finally, the patients in
this study had behavioral performance similar to normals on the
Sternberg task. Thus, the task-related theta activity does not appear
to be a consequence of the pathology of the subjects.
Concluding remarks
Elucidation of the properties of theta in rats progressed rapidly
because spatial exploration is such a reliable task for eliciting rat
theta. The present findings indicate that a working memory task is as
good at eliciting theta in humans as spatial exploration is in rats.
The highly reliable way in which human theta can be elicited by the
Sternberg task and the ability to precisely control the cognitive
demands of the task make this an ideal experimental system for the
further study of the role of theta in memory and cognition.
| |
FOOTNOTES |
Received Nov. 13, 2000; revised Jan. 10, 2001; accepted Jan. 26, 2001.
This work was supported by National Science Foundation Grant
IBN-9723466, National Institutes of Health Grant MH-55687, and the
Alfred P. Sloan Foundation.
We thank Larry Abbott, Xiao-Jing Wang, Marc Howard, and Adam Kepecs for
helpful comments on a previous version of this manuscript. We
acknowledge the enthusiastic cooperation of colleagues in the Children's Hospital Epilepsy Program, including Dr. Peter M. Black and
Lewis Kull. Finally, we are most grateful to the patients and their
families for their participation and support.
Correspondence should be addressed to John E. Lisman, Volen Center for
Complex Systems, Brandeis University, 415 South Street, Waltham, MA
02454-9110. E-mail: lisman{at}brandeis.edu.
| |
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TOP
ABSTRACT
INTRODUCTION
