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Ten- to 12-Hz EEG Oscillation in the Rat Hippocampus and Rhinal Cortex That Is Modulated by Environmental Familiarity

Ludk Nerad and David K. Bilkey

Department of Psychology, University of Otago, Dunedin, New Zealand

Submitted 2 March 2004; accepted in final form 19 October 2004

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

 
A characteristic feature of the electroencephalogram (EEG) of the hippocampus and rhinal (entorhinal and perirhinal) cortex of the freely moving rat is theta rhythm, a prominent oscillation of 8 Hz. Here we demonstrate that a novel rhythm that occurs at the border between the theta and alpha range of frequencies (10–12 Hz) can also be recorded from these structures. This rhythm (referred to here as "flutter") appears to be of non-theta origin as it can occur simultaneously with theta and it does not display the phase inversion across the hippocampus that characterizes theta activity. Flutter is observed in locomoting rats that are foraging for food reward in a familiar environment. Flutter disappears when rats are placed into a novel (although visually identical) environment, even though their foraging behavior does not appear to be altered. It is, at the present time, unclear what function flutter subserves. The presence of flutter may relate to a particular motivational state of the animal or to a particular type of information processing.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

 
Rhythmic electroencephalographic (EEG) activity can be recorded from a number of different brain regions as a reflection of synchronized firing in underlying networks of neurons. In regions of the brain that are involved in memory processing, such as the hippocampus, entorhinal and perirhinal cortex (Eichenbaum et al. 1999; Squire and Zola 1996), several forms of rhythmic EEG have been described. In the rat, these are usually categorized as falling into several frequency bands: delta (0–4 Hz), theta (4–12 Hz), beta (12–30 Hz), and gamma (>30 Hz). Some researchers also describe an alpha band that occurs between 8 and 12 Hz based on the classification utilized in human EEG studies, although in most of the animal literature, this frequency range is classified as being part of the theta band (Bland and Colom 1993).

Theta rhythm is the most prominent, and most researched, low-frequency oscillation in the hippocampus (Bland and Colom 1993; Buzsaki 2002; Green and Arduini 1954). It has previously been suggested that theta in the hippocampus and neighboring cortical regions may gate the flow and storage of information both within and between these areas (Chrobak and Buzsaki 1998; Winson 1990) during various forms of mnemonic processing (including spatial) (O'Keefe and Recce 1993; Shen et al. 1997; Skaggs et al. 1996).

In contrast to the wealth of research into the mechanisms and function of theta activity, there are relatively few investigations of in vivo activity in the range between 10 and 20 Hz. This is most likely a result of the fact that in the EEG of the freely moving rat, 8-Hz theta is usually far more prominent. Furthermore, most investigations of the 10- to 20-Hz range have utilized the in vitro hippocampal slice preparation (e.g., Traub et al. 1999). In this article, we describe a very regular EEG rhythm that occurs at 11 Hz. This rhythm can be recorded from the hippocampus and rhinal cortex of the freely moving rat as it forages in a familiar environment. We provide evidence to suggest that this rhythm is not theta activity, suggesting that it a form of activity that has not previously been described.

	METHODS

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Eighteen male Sprague-Dawley rats weighing between 295 and 360 g were chronically implanted for EEG, and in some cases single-unit, recording as described previously (Heynen and Bilkey 1991; Kyd and Bilkey 2003; Muir and Bilkey 2001). All procedures were conducted in accord with American Physiological Society guidelines and approved by the University Animal Ethics Committee. The rats were anesthetized with pentobarbital sodium and mounted in a Kopf stereotaxic instrument with the incisor bar such that bregma and lambda were in the same horizontal plane. Their skulls were exposed, and small holes were drilled for the anchor screws and for guide cannulae. The dura was pierced and electrodes or guide cannulae were lowered into position. The electrodes were connected to a head-socket, and the whole assembly was cemented to the skull with dental acrylic. The wound was tightly sutured around the socket, and the animal was allowed to recover. Moveable electrodes for single-unit (a bunch of 7 or 8 formvar-coated nichrome wires, 0.025 mm diam) and EEG recording (0.076 mm diam) were assembled singly or as a bundle inside the central guiding needle of the Scribe microdrive (Bilkey and Muir 1999; Bilkey et al. 2003). Stationary electrodes were located inside a guide cannula.

For hippocampal EEG recordings, electrodes were implanted into the dorsal and/or ventral hippocampus at AP –3.8, ML +2.5, DV 2.6 (from dura) or AP –5.6, ML +5.2, DV 7.5 relative to bregma, respectively. [Unless specified, all dorsoventral coordinates correspond to the scale relative to bregma, according to Paxinos and Watson (1998). The actual depth was always calculated from the atlas drawings, corrected for skull thickness, and measured from the skull unless specified otherwise.] One animal was implanted with one fixed and one movable EEG electrode (0.076 mm diam) in the dorsal hippocampus. The fixed electrode was located at AP –3.8, ML +2.5, DV 1.8 (from dura), and the other was connected to a movable microdrive. The tip of the movable electrode was initially located at AP –3.8, ML +2.4, DV 1.8 (from dura) and then moved ventrally. For hippocampal unit recordings, electrodes were initially implanted at AP –3.8, ML +2.5, DV 1.8 (from dura) and then moved ventrally in 41-µm steps.

For perirhinal cortex (PrhC) recording, the initial stereotaxic coordinates were AP –5.6, ML +6.4, DV 4.0 (from dura) and the electrode bundle was inclined at 11° laterally from vertical. These electrodes were then moved ventrally through perirhinal cortex in 41- or 82-µm steps on a session-by-session basis. In three rats, a tripolar EEG electrode, assembled in a movable microdrive, was implanted in the PrhC. This electrode was composed of three insulated wires arranged in a fork-like shape with the electrode tips located 400 µm apart so that recordings could be made from superficial, intermediate, and deep layers of the PrhC perpendicular to the cortical surface. The prongs of these electrodes were 2.3 mm long, such that any damage to cortex caused by the horizontal portions of the device was confined to regions located well-dorsal to the recording area. The coordinates of the middle electrode were initially AP –5.6, ML +6.4, DV 6.2. This electrode was moved through perirhinal cortex in 41- or 82-µm steps on a session-by-session basis. Two animals were chronically implanted with a movable bundle of seven electrodes (nichrome, 0.025 mm diam) initially positioned adjacent to the lateral entorhinal cortex for EEG recording. The stereotaxic coordinates of the tip of the longest electrode were AP –8.0, ML +5.2, DV 6.0. The bundle was oriented in a posterior direction at 30° from vertical and moved into entorhinal cortex.

At least 10 days after the surgery, the rats began a food-deprivation schedule (85–90% of baseline body weight) and were habituated to the experimental apparatus (a black plastic tub, 75 cm diameter with 50-cm high walls) for 3 days (10–15 min/day). Recordings were begun 2 wk postoperatively while the rats foraged freely for chocolate hail thrown onto the floor of the tub. During 10-min recording sessions EEG and/or unit activity was monitored through a flexible cable connected to the animal via a head plug. A tracking system sampled head position at 10 Hz and made this information available to the data-acquisition system. For some recordings, the tub that the animal had experienced for 9 days of habituation and recording was replaced by one that was novel but visually identical. In these recordings, animal speed and path sinuosity was analyzed and compared across the two environments.

EEG data were recorded with reference to a skull screw. The signal was buffered via an operational amplifier located in a head-stage mounted at the animal end of the recording cable. EEG activity was amplified, band-pass filtered between 1 and 20 Hz, and digitized at either 60 or 100 Hz. Rhythmic activity in the EEG was quantified using either a power spectrum analysis or with an automated wave-fitting analysis. Power spectra were calculated using Matlab. A Hanning window was applied. In the automated analysis, a custom computer program searched through the EEG data for amplitude peaks that were sufficiently separated that they could be part of either a theta (6–9.9 Hz) or flutter (10–15 Hz) oscillation. A cosine function was then correlated against every two-cycle section of the EEG that fitted this criterion. Where the correlation between the waveform and the function was above a preset value (0.3), theta or flutter was deemed to be present. For the purposes of the speed-EEG relationship analysis, a theta or flutter episode was defined as a continuous series of cycles that was >250 ms in length and that was separated from any other episode by 250 ms of non-theta, or flutter, activity. There was a high correspondence between the designations made by the computer program and those made by an experienced observer. This automated procedure was also used to determine the phase of firing of single units relative to this activity (Muir and Bilkey 2003).

To analyze the relationship between EEG activity and the speed of movement of the animal, the x and y coordinates of the rat were transformed into an absolute speed. Path jitter >10 Hz was digitally filtered out. Flutter waves in the hippocampal EEG were found with an automated routine similar to that described in the preceding text except that correlations were performed against a Morlet wavelet. Parameters were set conservatively to avoid false hits. Periods of EEG with flutter waves and >50 samples (6 flutter waves) were pooled, and the mean speed and power spectrum was calculated. A similar procedure was performed on nonflutter EEG. Although the sectioning and pooling of the EEG created some discontinuities in the data, there was no discernable effect of this procedure on the power spectrum within the theta and flutter range. In this analysis, all displayed power spectra were generated by averaging the individual spectra generated from a 256 sample or 512 sample (Fig. 8 only) moving window.

View larger version (29K): [in this window] [in a new window]   FIG. 8. Stacked spectral arrays generated from a sequence of 4 successive 10-min recording sessions (Jun27000–Jun27004) conducted in the same animal. Sessions are displayed in temporal order from left to right. Within-session time is on the y axis and power is on the z axis (identical scale in all sessions). EEG was recorded from the s.o. region of the hippocampus while the animal foraged freely. In the 1st and 3rd recordings, the rat was foraging in a familiar tub, in the 2nd and 4th in a visually identical, but novel, tub. Note that flutter disappeared in the novel environment to be replaced by theta but was reinstated by returning the animal to the familiar tub, even though foraging behavior appeared identical in both environments. The recordings were separated by 10 s during which the rat was held in the hand and the tub was replaced. Marks on the right of each graph represent the time at which fresh chocolate was thrown into the tub. Note that flutter frequency is higher at these times. Power spectra were generated from 512 sample blocks with the analysis window moving successively across the data in 5-sample increments. A Hanning window was applied.

To determine the strength of phase-locking and preferred phase of firing of a unit, or group of units to theta and flutter, the mean vector length and mean phase angle were calculated. Using rectangular coordinates based on the unit's (or group of units') phase of firing distribution, a vector from the origin of a circle (with a radius of 1) toward the circumference was calculated with length r (the mean vector length; note that r is corrected to account for the binning process) (Zar 1999) and direction (the mean phase angle) (Zar 1999). The mean vector length is effectively a measure of the concentration of the data points about the circumference of the circle and can range from 0 (where there was so much dispersion that no mean phase angle can be discerned) to 1 (where all the data points are concentrated at the same point on the circumference).

The Rayleigh's test (Zar 1999) was used to determine whether the phase of firing distribution for a unit (or group of units) was distributed evenly or unevenly (that is, it possessed a significant population mean direction) across the theta cycle. The Watson-Williams test (Zar 1999) was used to test whether two circular distributions were significantly different from each other.

	RESULTS

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General observations

We observed 10- to 12-Hz oscillations in the perirhinal cortex and/or hippocampus in all 18 freely moving rats once they were well habituated to the environment (Fig. 1). The oscillations were of near-sinusoidal morphology, had an amplitude of 100–500 µV and were confined to a narrow frequency range. Individual episodes of continuous oscillation typically lasted for 1 s although on occasion continuous bursts of >5-s duration were observed. Whereas in some recording sessions of 10-min duration there was little evidence of this rhythm, in other sessions (often in the same animal), it was present in a large proportion of the EEG trace (Table 1). An analysis of the power spectra confirmed that a large and narrow peak occurred at around 10–11 Hz during this EEG activity. This peak was well above, and clearly distinct from, the 7- to 8-Hz peak corresponding to theta rhythm. These two rhythms could thus be clearly differentiated as discrete phenomena in both the raw EEG waveform and the power spectrum (Fig. 1, A–C). Although the 10- to 11-Hz peak in the hippocampus is low compared with theta in the power spectrum shown in Fig. 1B (bottom), in other experiments we saw peaks that were larger than the theta-frequency peak. The amount of power at a certain frequency in the power spectrum will depend on the duration of the oscillation episode(s) within the total sample.

View larger version (28K): [in this window] [in a new window]   FIG. 1. A: a simultaneous recording of electroencephalogram (EEG) from the perirhinal cortex (PrhC) and dorsal hippocampus (d. Hipp) of a freely moving rat. Note the 11-Hz flutter activity in PrhC between the arrows. Theta activity can be observed in the d. Hipp with superimposed flutter. Horizontal bar, 1 s; vertical bars, PrhC: 0.1 mV, d. Hipp: 1.0 mV. B: power spectra calculated for a 1-min recording (of which A is a part). Recordings are from the dorsal hippocampus (upper) and perirhinal cortex (lower). Note the discrete peaks at 7.5 and 11 Hz. Vertical scales for power are identical. C: a simultaneous EEG recording from 3 different layers of the perirhinal cortex (PrhC, d.: deep, PrhC, i.: intermediate, PrhC, s.: superficial) and from the dorsal hippocampus (d. Hipp). Note the presence of flutter in the intermediate PrhC trace coincident with theta on the other electrodes. Horizontal bar, 1 s; vertical bars, all PrhC: 0.1 mV, d. Hipp: 2.0 mV. D: a power spectrum calculated for a 2.5-min recording from the hippocampus. Note the presence of peaks at 7.5, 10, and 15 Hz, corresponding to the theta fundamental, flutter, and the putative theta 2nd harmonic, respectively.

Our initial testing verified that the flutter waveform was not a result of aliasing as the activity could clearly be seen on an oscilloscope prior to the digitization procedure. Furthermore, altering the EEG sampling frequency from 60 to 100 Hz had no major effect on either the digitized waveform or the power spectrum. It was also determined that the frequency range of the flutter activity was not an artifact of the filter settings, as 8-Hz theta could clearly be seen as a discrete entity in the spectrum below the flutter activity and minimal power was observed in the band of frequencies between 12 and 20 Hz (e.g., see Fig. 1B). Furthermore, when low-pass filtering was shifted to 100 Hz, little change was observed in the flutter waveform, and there was minimal rhythmic activity at higher frequencies (relative to flutter). We also obtained evidence of flutter in another lab that used different operational amplifiers in the headstages and a different amplification and digitization system in the signal stream.

In other experiments, we recorded flutter from several other brain regions (Fig. 2), including the ventral hippocampus in three animals and the entorhinal cortex in two animals. Examples of flutter recorded from these regions are provided in Fig. 3. In each case, the appearance of the flutter waveform was similar across the different structures. In each of these structures, theta rhythm was sometimes registered as well. The two rhythms could occasionally be seen superimposed over each other (Fig. 1A, bottom) but more often either one or the other would dominate the local EEG activity. In some cases, transitions from the 8- to the 11-Hz rhythm (and vice versa) were observed to occur over several hundred milliseconds in the rhinal cortex and hippocampus. An example of such a transition is provided in Fig. 4. Flutter was also recorded in the dorsal hippocampus (18 animals), again sometimes interspersed with episodes of theta. We also observed that 11-Hz perirhinal cortex flutter could be recorded from one brain region while at the same time the 8-Hz theta rhythm could be recorded in the dorsal hippocampus (Fig. 5), leading us to believe that they were separate phenomena.

View larger version (34K): [in this window] [in a new window]   FIG. 2. Arrangement and placement of electrodes for EEG recording in the hippocampus, entorhinal cortex, and PrhC. —, the initial position of electrodes. Where movable electrodes were utilized, - - - denote the electrode track. A: a fixed and movable electrodes in stratum oriens (movable on left, fixed on right). B: a fixed electrode in the ventral hippocampus and a fork-shaped movable bundle of 3 electrodes initially implanted above the PrhC and then moved through this region. C: a movable bundle of 7 electrodes initially positioned rostral and dorsal to the entorhinal cortex and then moved through this region. Note: a fixed electrode that was implanted in the dorsal hippocampus at AP –3.8, ML +2.5, DV 2.6 (from dura) is not shown in B and C.

View larger version (15K): [in this window] [in a new window]   FIG. 3. Examples of flutter recorded separately from 4 brain regions. Most of the rhythmic activity in these recordings is occurring at a frequency of 10–12 Hz. Recordings were made separately from 4 different animals while they were performing a food foraging task. v. Hipp, ventral hippocampus; EC, entorhinal cortex. Horizontal bar, 1 s; vertical bars, PrhC: 0.5 mV, d. Hipp: 1.0 mV, v. Hipp: 1.0 mV, EC: 0.1 mV.

View larger version (35K): [in this window] [in a new window]   FIG. 4. An example of theta and flutter recorded simultaneously from stratum oriens (s.o.) and the hippocampal fissure (h.f.). Note that the theta activity that occurs in the 1st half is phase-shifted by 180° across the 2 electrodes. In comparison, the flutter occurring in the latter half is not phase-shifted. Horizontal bar, 1 s; vertical bar, 1.0 mV. Bottom: autocorrelograms (top trace) and cross-correlograms (bottom trace) of the 2 adjacent halves of EEG. (The amplitude was scaled to fit the graph). The vertical grid repeats at 100-ms intervals.

View larger version (17K): [in this window] [in a new window]   FIG. 5. Examples where theta and flutter could be recorded simultaneously from 2 different brain regions. Theta and flutter were recorded simultaneously in each pair of traces that display 1 s of waveform. The vertical scales are adjusted to optimize waveform size and are different in each channel. A: an example of clearly separated theta (T) and flutter (F) co-occurring in the hippocampus and perirhinal cortex, respectively. B and C: 2 examples of less clearly defined co-occurring theta and flutter activity recorded in the hippocampus and perirhinal cortex (B) and at the h.f. and s.o. of the hippocampus (C) respectively. In B and C, the arithmetic subtraction of the theta signal from the flutter signal (B) or flutter signal from the theta signal (C) produces a more sinusoidal waveform (bottom), suggesting that theta signals in the hippocampus may sometimes be "contaminated" by flutter activity and vice versa.

To investigate the relationship between flutter and theta, we measured the phase shift of signals recorded simultaneously from different depths in the dorsal hippocampus. Previous studies have shown that theta is phase-shifted by 180° between the stratum oriens and the hippocampal fissure (Brankack et al. 1993; Buzsaki et al. 1985). By comparing the signals recorded from a stationary electrode located in stratum oriens and a movable electrode, we were able to register depth-related changes in the EEG. As the movable electrode was gradually shifted deeper into the stratum radiatum, the 8-Hz theta rhythm underwent a phase shift of 180° in agreement with previous descriptions. Although in some recordings flutter was apparent in the stratum oriens but not at the deeper electrode, when both flutter and theta were recorded during the same session, we never observed a phase shift of the flutter activity (Fig. 4).

To determine how flutter was manifested across different depths of the perirhinal cortex, we recorded from a fork-like array of electrodes moved ventrally through the brain (n = 3 animals). Flutter was typically present on all three electrodes, but occasionally it was present on some electrodes and not others (Fig. 1C). We did not observe phase shifts between the electrodes.

Relationship of flutter to single-unit firing

Single-unit and EEG recordings were made in perirhinal cortex of freely moving rats (4 animals). The EEG was automatically separated into periods of low (6–10 Hz)- or high (10–15 Hz)-frequency oscillation, and the phase relationship between unit firing and EEG was determined. Twenty-five neurons generated >100 spikes (active cells) during theta activity, and 12 of these neurons (48%) showed significant phase-locking (P < 0.05, Rayleigh's test) to activity in this band (Fig. 6A). Twelve of 25 neurons (48%) generated >100 spikes during flutter activity, and 9 of these neurons (36% of the total and 75% of the active neurons) showed significant phase-locking (P < 0.05) to activity in the flutter band. Six of the active cells phase-locked to both theta and flutter. Overall, the preferred firing phase of perirhinal cells was significantly clustered (Rayleigh's z = 8.0, P < 0.0001) around a mean phase angle of 151° during theta activity (where zero degrees is the positive peak of the rhythm). Similarly, the firing clustered around 163° (Rayleigh's z = 4.6, P < 0.01) during flutter. There was no significant difference between these two phase angles (t = 0.68, Watson-Williams test). Overall, phase-locking was significantly stronger (as measured by vector length) during flutter (0.12 ± 0.01) compared with theta [0.08 ± 0.01; t(35) = 2.26, P < 0.05].

View larger version (37K): [in this window] [in a new window]   FIG. 6. Examples of the distribution of firing phase in perirhinal and hippocampal single units in relation to hippocampal theta (6–10 Hz) and flutter (10–15 Hz) frequencies in the EEG. The positive peak of the rhythm corresponds to 0/360°. A: the phase distribution of firing in a perirhinal single unit (8JA21000) to flutter and theta. Significant phase-locking was observed (P < 0.001, Rayleigh's test) to both rhythms with maximal firing occurring near the trough of the wave. B: the phase of firing of a hippocampal complex spike cell (09N21000). Significant phase-locking was observed (P < 0.005, Rayleigh's test;) to both theta and flutter. Similar mean phase angles were recorded for flutter and theta in most hippocampal units that phase-locked to both rhythms. An exception in illustrated in C, however, where a hippocampal complex spike cell displays significant phase-locking (P < 0.01, Rayleigh's test;) to both rhythms but fires maximally near the trough of theta and near the peak of the flutter waveform.

A further analysis of the hippocampal unit activity recorded with concurrent fissure-level EEG was conducted to ensure synchronization detected at flutter frequencies was not an artifact of our software inappropriately accepting theta as flutter. Toward these ends, the criterion for acceptance of flutter was raised to a 0.5 correlation with a cosine wave. Furthermore, portions of the waveform that were categorized as within the theta band (6–10 Hz) were removed from the analysis prior to the search for flutter activity. Three of 13 hippocampal units tested still showed significant phase locking (P < 0.05) to the flutter component of the waveform under these conditions.

Behavioral correlates

When hippocampal EEG was recorded from freely moving rats in consecutive sessions repeated over several days, a change in the relationship between flutter and theta was observed in all animals. Theta was dominant in the EEG during the first five to six recording sessions conducted in an initially novel environment. Flutter then began to appear in the EEG record, although its appearance in the hippocampus during any one recording session was unpredictable. Typical recording sessions were characterized either by strong, almost continuous, theta or by flutter of varying magnitude and duration with interspersed low and large-amplitude irregular activity. Observation of the relationship between the animal's movement and the appearance of flutter confirmed that this EEG activity appeared while the animal was moving and that it did not occur while the animal was stationary. Once the animal was moving, however, there did not appear to be a systematic relationship between the amount of flutter in the waveform and the mean velocity of the animal (Table 1). In one rat, a power spectrum was calculated for EEG data generated when the animal was stationary or moving at very low speeds (<7 cm/s); no flutter peak was apparent even though it was obvious when the full set of EEG data were analyzed (Fig. 7B). In a further analysis, a recording of 600-s duration was separated into flutter and nonflutter EEG using an automated procedure described in METHODS. The animal's speed during the nonflutter EEG was lower [8.8 ± 0.3 (SE) cm/s, 273 s of data] than during flutter activity (16.1 ± 0.4 cm/s, 191 s of data; see Fig. 7C). Identical analysis performed with another recording yielded similar values (6.6 ± 0.2 cm/s, 354 s, vs. 14.7 ± 0.5 cm/s, 85 s; Fig. 7D). The difference between speed during flutter and speed during the nonflutter EEG was significant [t-test, t(1,325) = 14.9, P < 0.001 and t(1,254) = 17.5, P < 0.001) in both cases.

View larger version (24K): [in this window] [in a new window]   FIG. 7. Flutter activity is associated with periods of movement of the animal. A: an example of flutter generated while the animal was locomoting across the environment. The graph illustrates the x and y coordinates of the animal's head and the concomitant hippocampal (Hipp) EEG activity. Horizontal bar, 1 s; vertical bars, top: 50 cm, bottom: 0.1 mV. B: power spectra calculated for EEG generated with different maximum speed thresholds (recording Jun27000; see Fig. 8). Left: EEG data generated only when the animal was stationary or moving at very low speeds (<7 cm/s) were analyzed. Middle: similar to left except that movement speed 70 cm/s is now included (= the whole data set). Right: the peak power within the 9- to 13-Hz frequency range for 6 different maximum speed thresholds. C and D: examples of 2 recordings (Jun27000 and Jun27002) where the data have been automatically separated into periods of nonflutter (left) and flutter (right) EEG. The bar graphs at the top of each section display the mean speed of the animal. The SE bars were too small to be shown. The associated power spectrum is displayed below. Note that average speed is lower during the nonflutter EEG. All vertical scales in B–D of "power" are in arbitrary units of power spectral density and are identical. The vertical scale of "speed" in C and D is 30 cm/s.

Flutter could be eliminated by other changes to the animal's familiar environment, such as placing it on the table that the tub sat on or by placing it inside its wire mesh home cage inside the tub. In both cases, the animal continued to eat chocolate during the manipulation and, in the former case, continued to forage normally. Flutter appeared to be quite closely associated with the appearance of chocolate within the chamber and the foraging behavior that accompanied this (Fig. 8).

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

 
The results of the present study demonstrate that strong near-sinusoidal oscillations with a frequency of between 10 and 12 Hz ("flutter") can be recorded from the rhinal cortex and hippocampus of the freely moving rat. Flutter could clearly be seen in the raw waveform as a strong independent oscillation and could not be accounted for by aliasing or other artifacts. It can be localized to regions of <400-µm radius while at the same time, these oscillations can be in synchrony with EEG activity in distant regions. Flutter can be observed in uninterrupted bursts that last for tens of seconds, but more commonly it occurs in a phasic manner with bursts of around 1- to 5-s duration. In the hippocampus, it can co-occur with theta, but it is masked or eliminated when high-amplitude theta occurs. Behavioral and environmental conditions under which flutter will be observed require locomotion in a familiar environment with reward (or its anticipation).

A question of critical interest relates to whether flutter is actually a form of theta activity. We argue that it is not on the basis of several pieces of evidence. First, our data show that lengthy episodes of 11-Hz flutter can often be recorded from rhinal cortex simultaneously with hippocampal theta activity of 8 Hz. This indicates that the two rhythms do not share a common pacemaker mechanism. In the case of theta, this pacemaker is thought to be located in the medial septum (see Vertes and Kocsis 1997 for a review). It is not known what mechanisms pace flutter activity. Second, the 8- and 11-Hz activity are not part of a continuum of varying frequencies. Rather, theta and flutter appear to represent two different and relatively independent states of the system. The frequency relationship between these two rhythms also indicates that flutter is not the previously described "second harmonic" of theta activity (Buzsaki et al. 1985). It is possible, however, that in some previous studies, investigators have observed a peak in the power spectrum at 10–12 Hz and inappropriately assumed that it is the second harmonic of theta. The actual second harmonic of theta is most likely the result of the nonsinusoidal shape of the theta fundamental (Leung 1984b; Leung et al. 1982). Unlike flutter, however, the second harmonic does not appear in the raw waveform as a clear oscillation, and the corresponding peak in the power spectrum is at twice the fundamental frequency of theta (around 14–16 Hz in a freely moving animal) (e.g., Buzsaki et al. 1985; Leung et al. 1982). We have records in which all three frequencies (8, 11, and 16 Hz) are clearly separated in the power spectrum (Fig. 1D). Third, a key characteristic of theta activity in the awake animal is that it is phase-inverted between the stratum oriens region of the hippocampus and the hippocampal fissure (Brankack et al. 1993; Buzsaki et al. 1986; Leung 1984a). Flutter does not display this characteristic suggesting that a different pattern of current sinks and/or sources underlie the generation of the two rhythms. In sum, these data indicate that flutter is not a high-frequency form of theta activity. Although "theta" oscillations at frequencies of between 10 and 12 Hz have previously been observed in the hippocampus of rats, in most of these studies, the high-frequency activity was induced by brain stimulation in anesthetized animals (Destrade and Ott 1981). In some other cases we believe that the theta that was observed may have actually been flutter.

Although in the present study flutter was recorded in the hippocampus and entorhinal and perirhinal cortex, it is possible that its occurrence is not limited to these regions. This raises the possibility that in some cases, it may be volume conducted (passive conduction through tissue via nonneural means) into these regions from a distally located generator. We cannot rule out this possibility on the basis of the present results; however, we believe that it is unlikely. For example, we found that when the EEG signals from rhinal cortex electrodes that were only several hundred micrometers apart were compared, on several occasions, they were different from each other in terms of the presence and/or absence of flutter (Fig. 1C). This indicates that volume conduction of flutter must be very limited in range at least within the rhinal cortex. Similarly, the phase inversion of theta within the hippocampus indicates how different the EEG signal in the hippocampus can be across short distances. That would not be possible if volume conduction was a strong phenomenon. Whether or not the EEG rhythm is a volume-conducted phenomenon, the finding that some units recorded within the hippocampus and perirhinal cortex were phase-locked to flutter indicates that there is synaptic input into these regions that is modulated at flutter frequencies and that this input has functional consequences for neuronal firing.

The data presented in the current study do not provide clear answers to the question of where flutter is generated and what region(s) act as the pacemaker for this activity. It is possible, for example, that the flutter recorded within the hippocampus is the result of synchronous and rhythmic neural firing in some other region that has neural projections to the former area. In this case, the current sources and sinks that are created in the hippocampus as a result of this external input produce the local EEG rhythm. Although one might expect that this would produce a phase inversion across the hippocampus, the failure to find an inversion does not rule out the possibility that the event has local consequences. For example, the theta waveform does not invert through mid stratum radiatum to mid stratum moleculare, yet the current source density (CSD) profile shows sources and sinks across this region (Brankack et al. 1993). Further work would be required to answer these questions.

At the present time it is unclear what mechanisms generate flutter activity. Phenomenologically, the EEG events that look most similar to flutter are sleep spindles. These are oscillations with a frequency of 7–14 Hz that occur in the thalamus and cortex during early stages of sleep (Contreras et al. 1997). Although it is possible that spindle activity may influence hippocampal activity during sleep (Sirota et al. 2003), there is no previous evidence to suggest this activity occurs in the awake, freely moving animal. This aside, however, similar mechanisms may generate both flutter and spindles. It is also possible that flutter activity is related to vibrissae movements (Harvey et al. 2001) or EEG oscillations in the olfactory system (Kay and Freeman 1998). If this were so, however, we might expect that both of these events would increase when an animal was placed into a novel environment rather than the contrary. Furthermore, previous studies of the frequency of vibrissae-associated EEG rhythms in the freely moving rat indicate that their energy peak is at 7–9 Hz, which is lower than flutter (see Berg and Kleinfeld 2003 for a review).

Although the function of flutter has yet to be determined, one recent report has shown that the degree of rhinal cortex and hippocampal EEG synchrony during memory acquisition is correlated with subsequent retrieval performance in humans (Fell et al. 2001). Previous studies show that the rhinal cortex region has both direct and indirect connections to the hippocampus (Burwell et al. 1995; Naber et al. 1999), an involvement in memory processes, including novelty/familiarity discrimination (Bussey et al. 1999; Fahy et al. 1993; Liu and Bilkey 2001; Murray and Richmond 2001; Suzuki et al. 1993), and intrinsic oscillatory properties (Bilkey and Heinemann 1999). It is possible, therefore that interactions between flutter and theta that occur between the hippocampus and rhinal cortex may serve a similar or related function (Lisman and Idiart 1995; O'Keefe and Recce 1993; Sauseng et al. 2002; Singer 1999).

	GRANTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

 
This work was supported by UOO-912 Grant from the Marsden Fund of New Zealand and by a University of Otago Research Grant.

	ACKNOWLEDGMENTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

 
We thank W. C. Abraham for constructive comments on an earlier version of this paper, N. Russell for useful discussion. Thanks to R. Kyd for providing equipment.

	FOOTNOTES

 
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Address for reprint requests and other correspondence: D. K. Bilkey, Dept. of Psychology, University of Otago, 95 Union St., Dunedin 9000, New Zealand (E-mail: dbilkey{at}psy.otago.ac.nz)

	REFERENCES

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

 
Berg RW and Kleinfeld D. Vibrissa movement elicited by rhythmic electrical microstimulation to motor cortex in the aroused rat mimics exploratory whisking. J Neurophysiol 90: 2950–2963, 2003.[Abstract/Free Full Text]

Bilkey DK and Heinemann U. Intrinsic theta-frequency membrane potential oscillations in layer III/V perirhinal cortex neurons of the rat. Hippocampus 9: 510–518, 1999.[CrossRef][ISI][Medline]

Bilkey DK and Muir GM. A low cost, high precision subminiature microdrive for extracellular unit recording in behaving animals. J Neurosci Methods 92: 87–90, 1999.[CrossRef][ISI][Medline]

Bilkey DK, Russell N, and Colombo M. A lightweight microdrive for single-unit recording in freely moving rats and pigeons. Methods 30: 152–158, 2003.[CrossRef][ISI][Medline]

Bland BH and Colom LV. Extrinsic and intrinsic properties underlying oscillation and synchrony in limbic cortex. Prog Neurobiol 41: 157–208, 1993.[CrossRef][ISI][Medline]

Brankack J, Stewart M, and Fox SE. Current source density analysis of the hippocampal theta rhythm: associated sustained potentials and candidate synaptic generators. Brain Res 615: 310–327, 1993.[CrossRef][ISI][Medline]

Burwell RD, Witter MP, and Amaral DG. Perirhinal and postrhinal cortices of the rat: a review of the neuroanatomical literature and comparison with findings from the monkey brain. Hippocampus 5: 390–408, 1995.[CrossRef][ISI][Medline]

Bussey TJ, Muir JL, and Aggleton JP. Functionally dissociating aspects of event memory: the effects of combined perirhinal and postrhinal cortex lesions on object and place memory in the rat. J Neurosci 19: 495–502, 1999.[Abstract/Free Full Text]

Buzsaki G. Theta oscillations in the hippocampus. Neuron 33: 325–340, 2002.[CrossRef][ISI][Medline]

Buzsaki G, Czopf J, Kondakor I, and Kellenyi L. Laminar distribution of hippocampal rhythmic slow activity (RSA) in the behaving rat: current-source density analysis, effects of urethane and atropine. Brain Res 365: 125–137, 1986.[CrossRef][ISI][Medline]

Buzsaki G, Rappelsberger P, and Kellenyi L. Depth profiles of hippocampal rhythmic slow activity ("theta rhythm") depend on behavior. Electroencephalogr Clin Neurophysiol 61: 77–88, 1985.[CrossRef][ISI][Medline]

Chrobak JJ and Buzsáki G. Operational dynamics in the hippocampal-entorhinal axis. Neurosci Biobehav Rev 22: 303–310, 1998.[CrossRef][ISI][Medline]

Contreras D, Destexhe A, Sejnowski TJ, and Steriade M. Spatiotemporal patterns of spindle oscillations in cortex and thalamus. J Neurosci 17: 1179–1196, 1997.[Abstract/Free Full Text]

Destrade C and Ott T. Quantitative analysis of hippocampal rhythmical slow activity induced by stimulation of different diencephalic structures. Neuroscience 6: 1089–1094, 1981.[CrossRef][ISI][Medline]

Eichenbaum H, Dudchenko P, Wood E, Shapiro M, and Tanila H. The hippocampus, memory, and place cells: is it spatial memory or a memory space? Neuron 23: 209–226, 1999.[CrossRef][ISI][Medline]

Fahy FL, Riches IP, and Brown MW. Neuronal activity related to visual recognition memory: long-term memory and the encoding of recency and familiarity information in the primate anterior and medial inferior temporal and rhinal cortex. Exp Brain Res 96: 457–472, 1993.[ISI][Medline]

Fell J, Klaver P, Lehnertz K, Grunwald T, Schaller C, Elger CE, and Fernandez G. Human memory formation is accompanied by rhinal-hippocampal coupling and decoupling. Nat Neurosci 4: 1259–1264, 2001.[CrossRef][ISI][Medline]

Green JD and Arduini A. Hippocampal electrical activity in arousal. J Neurophysiol 17: 533–557, 1954.[Free Full Text]

Harvey MA, Sachdev RN, and Zeigler HP. Cortical barrel field ablation and unconditioned whisking kinematics. Somatosens Mot Res 18: 223–227, 2001.[CrossRef][ISI][Medline]

Heynen AJ and Bilkey DK. Induction of RSA-like oscillations in both the in vitro and in vivo hippocampus. Neuroreport 2: 401–404, 1991.[CrossRef][ISI][Medline]

Kay LM and Freeman WJ. Bidirectional processing in the olfactory-limbic axis during olfactory behavior. Behav Neurosci 112: 541–553, 1998.[CrossRef][ISI][Medline]

Kyd RJ and Bilkey DK. Prefrontal cortex lesions modify the spatial properties of hippocampal place cells. Cereb Cortex 13: 444–451, 2003.[Abstract/Free Full Text]

Leung LS. Pharmacology of theta phase shift in the hippocampal CA1 region of freely moving rats. Electroencephalogr Clin Neurophysiol 58: 457–466, 1984a.[ISI][Medline]

Leung LS. Theta rhythm during REM sleep and waking: correlations between power, phase and frequency. Electroencephalogr Clin Neurophysiol 58: 553–564, 1984b.[CrossRef][ISI][Medline]

Leung LS, Lopes da Silva FH, and Wadman WJ. Spectral characteristics of the hippocampal EEG in the freely moving rat. Electroencephalogr Clin Neurophysiol 54: 203–219, 1982.[CrossRef][ISI][Medline]

Lisman JE and Idiart ME. Storage of 7 +/- 2 short-term memories in oscillatory subcycles. Science 267: 1512–1515, 1995.[Abstract/Free Full Text]

Liu P and Bilkey DK. The effect of excitotoxic lesions centered on the hippocampus or perirhinal cortex in object recognition and spatial memory tasks. Behav Neurosci 115: 94–111, 2001.[CrossRef][ISI][Medline]

Muir GM and Bilkey DK. Instability in the place field location of hippocampal place cells after lesions centered on the perirhinal cortex. J Neurosci 21: 4016–4025, 2001.[Abstract/Free Full Text]

Muir GM and Bilkey DK. Theta- and movement velocity-related firing of hippocampal neurons is disrupted by lesions centered on the perirhinal cortex. Hippocampus 13: 93–108, 2003.[CrossRef][ISI][Medline]

Murray EA and Richmond BJ. Role of perirhinal cortex in object perception, memory, and associations. Curr Opin Neurobiol 11: 188–193, 2001.[CrossRef][ISI][Medline]

Naber PA, Witter MP, and Lopez da Silva FH. Perirhinal cortex input to the hippocampus in the rat: evidence for parallel pathways, both direct and indirect. A combined physiological and anatomical study. Eur J Neurosci 11: 4119–4133, 1999.[CrossRef][ISI][Medline]

O'Keefe J and Recce ML. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3: 317–330, 1993.[CrossRef][ISI][Medline]

Paxinos G and Watson C. The Rat Brain in Stereotaxic Coordinates (4th ed.). San Diego, CA: Academic, 1998.

Sauseng P, Klimesch W, Gruber W, Doppelmayr M, Stadler W, and Schabus M. The interplay between theta and alpha oscillations in the human electroencephalogram reflects the transfer of information between memory systems. Neurosci Lett 324: 121–124, 2002.[CrossRef][ISI][Medline]

Shen J, Barnes CA, McNaughton BL, Skaggs WE, and Weaver KL. The effect of aging on experience-dependent plasticity of hippocampal place cells. J Neurosci 17: 6769–6782, 1997.[Abstract/Free Full Text]

Singer W. Neuronal synchrony: a versatile code for the definition of relations? Neuron 24: 49–65, 1999.[CrossRef][ISI][Medline]

Sirota A, Csicsvari J, Buhl D, and Buzsaki G. Communication between neocortex and hippocampus during sleep in rodents. Proc Natl Acad Sci USA 100: 2065–2069, 2003.[Abstract/Free Full Text]

Skaggs WE, McNaughton BL, Wilson MA, and Barnes CA. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus 6: 149–172, 1996.[CrossRef][ISI][Medline]

Squire LR and Zola SM. Structure and function of declarative and nondeclarative memory systems. Proc Natl Acad Sci USA 93: 13515–13522, 1996.[Abstract/Free Full Text]

Suzuki WA, Zola-Morgan S, Squire LR, and Amaral DG. Lesions of the perirhinal cortex and parahippocampal cortices in the monkey produce long-lasting memory impairment in the visual and tactual modalities. J Neurosci 13: 2430–2451, 1993.[Abstract]

Traub RD, Whittington MA, Buhl EH, Jefferys JGR, and Faulkner HJ. On the mechanism of the frequency shift in neuronal oscillations induced in rat hippocampal slices by tetanic stimulation. J Neurosci 19: 1088–1105, 1999.[Abstract/Free Full Text]

Vertes RP and Koscis B. Brainstem-diencephalo-septohippocampal systems controlling the theta rhythm of the hippocampus. Neuroscience 81: 893–926, 1997.[CrossRef][ISI][Medline]

Winson J. The meaning of dreams. Sci Am 263: 86–96, 1990.[ISI][Medline]

Zar JH. Circular distributions: hypothesis testing. In: Biostatistical Analysis (4th ed.). Upper Saddle River, NJ: Prentice Hall, 1999, chapt. 27, p. 616–630.

Zironi I, Iacovelli P, Aicardi G, Liu P, and Bilkey DK. Prefrontal cortex lesions augment the location-related firing properties of area TE/perirhinal cortex neurons in a working memory task. Cereb Cortex 11: 1093–1100, 2001.[Abstract/Free Full Text]

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