Patients with Alzheimer's disease often forget where they have been or who they have just met. This happens because the neurons in those areas of the brain where memories are processed are dying. Indeed, by the time Alzheimer's disease has been diagnosed, many of the neurons in these regions have already died. The symptoms of Alzheimer's disease are then produced by the remaining neurons. However, the reasons why the remaining neurons cannot make new memories are unknown.

In normal mice the neurons in the hippocampus, a part of the brain that is important for memory, are called ‘place cells’ because they are turned on when the mouse is in a specific place. As a consequence, when the mice moves around, different neurons are turned on one by one, and this sequence of activation is believed to be a memory code that represents the places the animal has travelled.

Cheng and Ji have explored this phenomenon in mice that have been genetically engineered so that their neurons contain structures called ‘tau tangles’ that are thought to be involved in the death of neurons. More importantly, these transgenic mice suffer age-dependent neuron loss in a way that is similarly to people with Alzheimer's disease.

Cheng and Ji implanted tiny sensors into the hippocampus of these mice, and used these sensors to monitor the activity of the remaining hippocampal neurons as the mice moved around while searching for food. They found that the neurons were activated almost everywhere, which indicates that the hippocampal neurons in transgenic mice are no longer working as place cells. However, these neurons were still activated one by one in robust sequences. Moreover, the sequences generated by the transgenic mice were the same in many different surroundings, which suggests that these sequences are not memory codes of the animal's current surroundings. Cheng and Ji propose that the sequences reflect existing memories already stored in the brain, which would suggest that Alzheimer's patients cannot form new memories because the brain is preoccupied by old memories, and thus fails to store the new information that is coming in from the outside world.

Alzheimer's disease (AD) is behaviorally characterized by cognitive declines such as memory loss (Carlesimo and Oscar-Berman, 1992; Salmon and Bondi, 2009), but pathologically defined by β-amyloid plaques and tau pathology including neurofibrillary tangles and tau-mediated neurodegeneration (Wenk, 2003; Ashe and Zahs, 2010). A current challenge is how to bridge between these two very different aspects of AD. Because memory deficits are necessarily produced by the memory-processing neural circuits in vivo (Dickerson and Eichenbaum, 2010), a key step toward this challenge is to understand the functional alterations in the memory circuits, including the hippocampus (HP), of the living brain with ongoing pathogenesis. Both the β-amyloid and tau pathologies are likely involved in the memory loss (Chapman et al., 1999; Yu et al., 2001; Cacucci et al., 2008; Ashe and Zahs, 2010; Palop and Mucke, 2010). In particular, tau pathology and subsequent neurodegeneration play a key role in mediating the memory symptoms seen at a mature stage of AD (Ashe and Zahs, 2010). Currently, it is unknown how the mnemonic functions of HP are altered by tau pathology in vivo.

One well-studied function of HP in vivo is the coding of spatial memories. It is believed that spatial memories in both rodents and humans are encoded by HP place cells that fire at one or a few specific locations of a given space (place fields) (O’Keefe and Dostrovsky, 1971; Wilson and McNaughton, 1993; Burgess and O'Keefe, 2003; Ekstrom et al., 2003). When the animal runs along a novel spatial trajectory, external sensory input at individual locations along the trajectory drives place cells to fire one after another in a unique sequence, which yields a memory code for the trajectory (Wilson and McNaughton, 1993; Harris et al., 2003; Dragoi and Buzsaki, 2006). After the novel experience, the sequence becomes internally established and can be reactivated during later memory recall and/or consolidation with or without partial external input (Buzsaki, 1989; Wilson and McNaughton, 1994; Skaggs and McNaughton, 1996; Nadasdy et al., 1999; Lee and Wilson, 2002; Foster and Wilson, 2006; Diba and Buzsaki, 2007; Ji and Wilson, 2007; Gelbard-Sagiv et al., 2008; Davidson et al., 2009; Karlsson and Frank, 2009; Carr et al., 2011). Therefore, in theory, on any given trajectory HP could either retrieve stored internal sequences, form new ones, or combine both, based on the external input that HP receives from the environment. It has been proposed that the interplay between internal and external inputs is important to memory processing in HP and abnormal interaction may lead to memory interference and/or intrusion (Nakazawa et al., 2002; McHugh et al., 2007; Colgin et al., 2008).

The availability of tauopathy animal models provides an opportunity to study how tau pathology alters HP mnemonic functions in vivo. To this end, we set out to examine the spatial memory code in the transgenic rTg4510 (Tau) mouse. This model was chosen because its well-characterized pathological and behavioral phenotypes mimic many key features of tau pathology in AD (Ramsden et al., 2005; Santacruz et al., 2005; Berger et al., 2007). In Tau mice, the overexpression of a mutated version (P301L) of the human tau gene restricted in the forebrain leads to age-dependent spatial memory deficits, tau neurofibrillary tangles, and neuronal loss. In this study, we recorded neurons in the CA1 region of HP using the tetrode recording technique (Wilson and McNaughton, 1993; Buzsaki, 2004; Ji and Wilson, 2007) from 7 to 9 month old Tau mice and their control littermates while they performed spatial navigation tasks. We found that HP neurons in Tau mice fail to fire at specific spatial locations and yet maintain robust firing sequences, suggesting that non-spatial, internal brain activities dominate the HP circuits and prevent them encoding external space.

We recorded a total of 497 CA1 neurons from eight Tau and six wildtype (WT) control mice, all at 7–9 months old (Table 1). Confirming previous studies (Ramsden et al., 2005; Santacruz et al., 2005), there was massive neurodegeneration in Tau mice at this age and the thickness of the CA1 pyramidal layer in HP of Tau mice was on average only 48% of that of WT mice (Figure 1 and Table 1).

Figure 1

Download asset Open asset

CA1 neurons in Tau mice did not fire at specific locations on a familiar track

First, we asked whether CA1 place cell activities were altered in Tau mice when they ran along a familiar rectangular track (Figure 2A) two sessions a day, 15 min each session. In each session, the animals ran back and forth (two trajectories) and repeated 10–40 laps on each trajectory. We analyzed 87 putative pyramidal neurons from WT mice (WT neurons) and 93 from Tau mice (Tau neurons) that were active on at least one trajectory in at least one session. The median firing rate of Tau neurons during the track running sessions was not significantly different from that of WT neurons (median and [10% 90%] range rate: WT 1.21 [0.43 3.60] Hz, Tau 0.90 [0.41 2.75] Hz, p=0.08, ranksum-test, same below unless specified otherwise).

Figure 2

Download asset Open asset

As expected, WT neurons fired spikes at stable, specific locations in every lap that the animal traversed a trajectory. In contrast, spikes of Tau neurons were not confined to specific locations (Figure 2B). Close visual inspections revealed that Tau neurons’ spikes were still specific to one or a few locations in each individual lap, but the locations were changed from lap to lap (Figure 2B). We described a neuron’s firing profile on a trajectory by a rate curve (the two trajectories were analyzed separately for each neuron), which was the neuron’s firing rate as a function of the position along the trajectory. The rate curve was computed for every lap and then averaged across all laps. Whereas the averaged rate curves of typical WT neurons clearly showed one or a few peaks indicative of well-defined place fields, those of typical Tau neurons did not show such prominent peaks (Figure 2B, ‘bottom curves’). We quantified the location-specificity using spatial information (SI) (Skaggs et al., 1993). SI was computed for each neuron active on each trajectory at two levels, a lap SI to measure the location-specificity at individual lap level and a trajectory SI to measure the overall location-specificity across all laps based on its averaged rate curve. The median lap SI of WT neurons was only slightly (21%) greater than that of Tau neurons (Figure 2C; WT 2.03 [1.00 3.09] bits/spike, N = 263; Tau 1.68 [0.84 2.34] bits/spike, N = 262; p=7.7 × 10⁻¹¹), but the median trajectory SI of WT neurons was much greater (180%) than that of Tau neurons (Figure 2D; WT: 1.29 [0.55 2.61] bits/spike; Tau: 0.46 [0.15 1.29] bits/spike; p=5.3 × 10⁻³⁹). We also quantified the lap-to-lap location change by rate-stability, defined as the average correlation coefficient between any two laps’ rate curves. Tau neurons showed much lower rate-stability than WT neurons (Figure 2E; WT: 0.66 [0.30 0.86]; Tau: 0.075 [0.0042 0.34]; p=1.7 × 10⁻⁷⁰). These results reveal that Tau neurons lost their overall location-specificity, mainly due to unstable firing locations.

CA1 neurons in Tau mice maintained robust firing sequences on the familiar track

Because WT neurons fired at stable locations, they fired one after another with a stable, position-locked sequence in every lap of a trajectory (Figure 3A). To our surprise, despite their unstable firing locations, Tau neurons still maintained stable firing sequences, that is, they fired with consistent orders across laps (Figure 3A).

Figure 3 with 4 supplements see all

Download asset Open asset

To quantitatively demonstrate this, we analyzed the high-order (≥4 neurons) firing sequences (Lee and Wilson, 2002; Ji and Wilson, 2007) of WT/Tau neurons on the familiar track. We first assigned letter identities to those neurons active on a trajectory. We arranged pairs of neurons according to their firing orders identified by their cross-correlations. We then derived a template sequence for the trajectory that agreed with all the ordered pairs. For example, for the five Tau neurons shown in Figure 3A, the cross-correlation between the second (assigned as neuron A) and the fourth (neuron H) had a positive peak at 0.8 s. We ordered them as AH. By ordering all pairs among the five neurons in this manner, we derived a template sequence FABHD for this trajectory. A total of 13 template sequences were derived from WT mice and 8 from Tau mice (Table 2). For a template sequence on a trajectory, we then detected the firing sequences in the spiking patterns of all laps that matched with the template. To evaluate the significance of the detection, we generated 1000 shuffled copies of the spike patterns by randomly swapping neuron identities and then detected the number of sequences in each copy. The number of detected sequences in the real spike patterns was expressed as a Z-score computed from the mean and standard deviation of the shuffling-generated counts. We found a significant number of detected sequences for 9 out of the 13 WT templates and 6 of the 8 Tau templates (p<0.05, Z-test; Table 2). The mean (±SEM) Z-scores for all WT and all Tau templates were similarly high (Figure 3B; WT: 2.5 ± 0.3; Tau: 2.3 ± 0.3; p=0.67, one tailed t-test, same below unless specified otherwise). Both WT and Tau template sequences were detected in almost every lap (Figure 3A). Although it appeared that Tau sequences occurred more frequently than WT sequences in a given lap (Figure 3A), this difference was not significant (Figure 3C; WT: 1.0 ± 0.1 times per lap; Tau: 1.4 ± 0.3 times per lap; p=0.10). These results show that Tau neurons, like WT neurons, formed robust firing sequences.

Table 2

Template sequence	Animal name	Genotype	Self traj.	Z (S1T1)	Z (S1T2)	Z (S2T1)	Z (S2T2)	Z (O1)	Z (O2)
AGFHJ	AN4	WT	S2T2	0.01	2.8	−0.4	3.2	NA	NA
ZSBPNA	AN19	WT	S1T1	2.6	−0.7	1.4	0.3	0.8	−1.7
JHLKZE	AN19	WT	S1T2	1.6	2.7	−0.4	1.8	−0.1	0.6
IYOBZENA	AN19	WT	S2T1	1.9	2.3	3.5	1.3	0.7	2.7
ZSaFbB	AN19	WT	S2T2	−0.3	1.7	0.9	1.9	1.9	0.3
GHKJBDF	AN20	WT	S1T1	1.6	1.2	2.4	1.0	−1.8	0.9
JHAKC	AN20	WT	S1T2	−0.6	1.3	−0.3	2.1	0.02	−0.7
GHCKJBDF	AN20	WT	S2T1	1.8	0.3	1.4	1.3	−0.6	1.4
GJAHLC	AN20	WT	S2T2	0.5	1.1	0.4	0.8	−1.8	−0.3
XIGQOZ	AN21	WT	S1T1	4.0	−1.2	3.6	−1.0	−2.0	−1.0
IWQVHP	AN21	WT	S1T2	−0.8	3.0	−1.2	3.3	0.2	0.0
GOZTHR	AN21	WT	S2T1	1.9	0.5	3.6	0.3	1.0	−1.5
OFINKDV	AN21	WT	S2T2	−0.9	3.8	0.5	3.3	−0.5	−1.4
ACHFDG	AN5	Tau	S2T1	3.0	−0.4	2.5	−0.06	−1.2	−1.6
FABHD	AN13	Tau	S1T1	4.0	−0.1	2.9	2.0	2.1	2.9
BFAHD	AN13	Tau	S2T1	1.7	1.1	2.1	−0.9	0.04	0.5
FEBA	AN15	Tau	S2T1	1.0	0.9	3.2	−0.4	2.6	1.9
ILFBK	AN22	Tau	S1T1	1.4	0.5	−0.8	−0.8	1.2	−0.5
FLBDIC	AN22	Tau	S2T1	0.1	1.4	2.2	0.7	2.2	2.9
PKLEDO	AN25	Tau	S1T2	1.1	1.9	0.4	3.3	2.4	2.4
PKTLEOMA	AN25	Tau	S2T2	0.7	1.8	0.0	1.3	3.4	1.9

1. Template sequence: each letter (case-sensitive) represents a cell; same letters across different templates but within the same animal represent the same cells. Self traj.: The trajectory where the template was derived. Z: Number of detected matches (in Z-score) with a template sequence on a trajectory or in an open box session. S1T1: Trajectory one of session one, S1T2: Trajectory two of session one, so forth; O1: First open box session, so forth; numbers on self-trajectories are in bold; numbers that are statistically significant (p<0.05) are in red. NA: not available (not all experiments included two trajectory running sessions and two open box sessions).

However, there was a major difference between WT and Tau sequences. Whereas WT template sequences predominantly occurred at similar locations across laps, Tau sequences shifted their locations from lap to lap (Figure 3A). The mean location shift of the detected Tau template sequences was significantly greater than that of WT sequences (Figure 3D; WT: 12.7 ± 1.9 cm; Tau: 31.3 ± 6.2 cm; p=0.0035). Therefore, unlike WT sequences, Tau sequences were not anchored to specific spatial locations.

The firing sequences of CA1 neurons in Tau mice seen on the familiar track also appeared during free exploration of a familiar open box

We have shown that CA1 neurons maintained robust firing sequences during track running, even though they did not fire at specific locations. We next asked whether this was true during a very different behavior in a different familiar space. After completing the track running sessions, we kept recording the same CA1 neurons while the mice freely explored a familiar open box (Figure 4A) for one to two sessions, 15 min each session.

Figure 4

Download asset Open asset

We analyzed 74 WT and 68 Tau neurons that were active in at least one open box session. The median firing rate of these Tau neurons was lower than that of WT neurons (WT 1.3 [0.5 3.8] Hz, Tau 0.9 [0.5 2.7] Hz, p=0.0001), which would predict a greater location-specificity of Tau neurons. However, we observed the opposite. As illustrated by their rate maps (Figure 4B), firing rate color-plotted at each position of the two dimensional (2D) open box, firing activities of Tau neurons covered a much larger portion of the box (meaning less location-specific) than those of WT neurons. The location-specificity in the box was quantified by SI in the 2D open space (open SI) for each neuron active in each open box session. The median open SI of WT neurons was significantly greater than that of Tau neurons (Figure 4C; WT: 0.24 [0.04 0.76], N = 131; Tau: 0.11 [0.04 0.34], N = 119; p=1.9 × 10⁻⁷). The result indicates that Tau neurons fired with low location-specificity in the familiar open box.

We then examined whether there were organized firing sequences of multiple neurons during the open box sessions. Because of the random nature of the free exploration behavior, unlike the repeated trajectory running on the track, we did not expect repetitive high-order firing sequences to appear in the open box. However, when we arranged the neurons according to their firing orders in the template sequences derived during track running, it became apparent that Tau neuron sequences, but not WT sequences, during the track running also appeared frequently during the open box sessions (Figure 5A). We quantified this observation by searching through the spike patterns of open box sessions for sequences that matched with the trajectory template sequences. Here we refer ‘a search’ as to matching one open box session to one trajectory template. We found a significant number of sequences in 10 out of the 16 searches in Tau mice, whereas only in 2 of the 24 searches in WT mice (p<0.05, Z-test; Table 2). The mean Z-score of all searches in Tau mice was significantly higher than that in WT mice (Figure 5B; WT: −0.1 ± 0.3; Tau: 1.4 ± 0.4; p=0.001). Therefore, Tau sequences, but not WT sequences, seen on the track trajectories also appeared robustly during the open box sessions.

Figure 5 with 1 supplement see all

Download asset Open asset

Although the animals were allowed to move around freely in the open box, it is possible that some mice ran certain paths repeatedly and resulted in repeatedly occurring firing sequences. To examine this possibility, we plotted the animal’s paths between the start and end times of all detected Tau sequences in a search. These paths appeared to be randomly distributed in the box (Figure 5C), suggesting that the detected sequences were not linked to particular locations or a particular path. To quantitatively demonstrate this, we drew a vector from the start to the end location of each path. We then plotted all the vectors resulted from a search with their starting positions aligned at the origin (Figure 5D). If the paths were not randomly distributed, the vectors should show a preferred direction. However, the vectors of a typical search were randomly orientated (Figure 5D). For each search in Tau mice where a significant number of sequences were found, we computed a Rayleigh p value, which tested a null hypothesis that the vectors had no preferred direction. None of the p values for all the 10 searches was significant (Figure 5E, p≥0.07), indicating that the null hypothesis could not be rejected. Therefore, the detected Tau sequences in open box sessions were not due to repetitive running behavior. Together with the low location-specificity of Tau neurons in the open box sessions (Figure 4), we concluded that Tau sequences seen in the open box were not primarily driven by spatial locations. Even further, these Tau sequences seen in the open box were the same as those during the track running. Therefore, Tau sequences did not distinguish two completely different spaces during two very different behaviors. This is a striking result given the well-known phenomenon of ‘remapping’ of place cell activities between different environments (Muller and Kubie, 1987; Leutgeb et al., 2005; Colgin et al., 2008).

Robust firing sequences of CA1 neurons in Tau mice also appeared in novel environments

We have demonstrated that Tau neurons displayed robust firing sequences on a familiar track and in a familiar open box despite their apparent lack of spatial specificity. To understand whether this peculiar feature of Tau neurons requires spatial experience, we also recorded CA1 neurons while mice explored a novel open box for one to two sessions and then ran back and forth (two trajectories) on a novel track for one to two sessions in a novel room. The findings were similar as those seen in the familiar environments.

On the novel track (Figure 6A), 79 WT neurons and 92 tau neurons were active on at least one of its trajectories. Their median firing rates during the novel track running sessions were not significantly different (WT: 1.4 [0.4 2.7] Hz, Tau: 1.1 [0.4 3.3] Hz, p=0.71). Whereas the firing locations of WT neurons became stabilized quickly on the novel trajectories, those of Tau neurons stayed unstable from lap to lap (Figure 6B). As a result, the median trajectory SI of Tau neurons was much lower than that of WT neurons (WT 0.69 [0.23 1.5] bits/spike, Tau 0.28 [0.086 0.68] bits/spike, p=5.9 ×1 0⁻³¹), despite a similar median lap SI (WT 1.2 [0.58 1.8] bits/spike, Tau 1.2 [0.46 1.6] bits/spike, p=0.62). The rate-stability of Tau neurons was much lower than that of WT neurons (WT 0.47 [0.17 0.85], Tau 0.10 [0.03 0.28], p=1.6 × 10⁻⁵¹). In the novel open box (Figure 6C), 74 WT neurons and 61 Tau neurons were active in at least one session. The median firing rates of these WT and Tau neurons during the novel box sessions were not significantly different (WT: 1.1 [0.4 2.9] Hz, Tau: 1.0 [0.5 2.6] Hz, p=0.38). The rate maps of Tau neurons showed much broader firing areas than those of WT neurons (Figure 6D), suggesting a reduced location-specificity in Tau neurons. This was confirmed by the result that the median open SI of Tau neurons was significantly lower than that of WT neurons (WT: 0.28 [0.06 0.71] Hz, Tau: 0.08 [0.02 0.32] Hz, p=1.3 × 10⁻¹²). These results show that Tau neurons fired with little location-specificity either on the novel trajectories or in the novel open box.

Figure 6

Download asset Open asset

Nevertheless, Tau neurons, like WT neurons, fired with robust sequences on the novel trajectories. But unlike WT neuron sequences, Tau sequences on the novel trajectories were not anchored to specific locations (Figure 6E) and same sequences also appeared in the novel open box sessions (Figure 6F). We derived 15 high-order template sequences on the novel trajectories from WT mice and 8 from Tau mice (Table 3). The mean Z-scores of WT and Tau template sequences on the novel trajectories were similarly high (Figure 6G, WT: 2.5 ± 0.2, Tau: 2.4 ± 0.4, p=0.91), but the mean location shift of Tau sequences among laps was greater than that of WT ones (Figure 6H; WT: 9.2 ± 0.9 cm, Tau: 18.5 ± 3.6 cm, p=0.0038). The mean location shift values of both WT and Tau sequences shown here were smaller than those on the familiar track, probably due to the shorter length of the novel track. Finally, Tau template sequences were detected in the novel open box sessions significantly more often than WT templates (Figure 6I, mean Z-scores: WT: 0.5 ± 0.2, N = 30 searches; Tau: 1.5 ± 0.3, N = 12; p=0.0029). Here we emphasize that the novel open box sessions preceded the novel track running. Therefore, Tau sequences seen on the novel trajectories were already preexistent in the prior open box sessions. These findings suggest that the formation of Tau sequences did not primarily depend on spatial experience in an environment.

Table 3

Template sequence	Animal name	Genotype	Self traj.	Z (S1T1)	Z (S1T2)	Z (S2T1)	Z (S2T2)	Z (O1)	Z (O2)
IHFGE	AN18	WT	S1T1	3.4	0.1	1.4	0.2	1.0	1.1
HEIFG	AN18	WT	S1T2	0.4	4.3	−1.4	2.7	1.6	−0.8
HICFBG	AN18	WT	S2T2	1.9	3.5	−0.3	3.6	2.5	0.8
NAOJBD	AN19	WT	S1T1	2.5	−1.5	1.4	0.1	0.1	−0.1
OKDHFBAJN	AN19	WT	S1T2	−0.1	1.6	−1.0	0.7	1.1	0.1
LANOB	AN19	WT	S2T1	3.1	−0.6	3.0	−1.1	−0.5	−0.4
OKFCNA	AN19	WT	S2T2	−0.4	1.3	−0.4	1.7	1.6	0.1
ISRPBGJXKQAY	AN20	WT	S1T1	1.5	−1.6	2.0	1.2	0.9	−1.1
BIPRS	AN20	WT	S1T2	−0.1	1.1	0.2	−0.3	0.3	0.2
CTDOISREBXG	AN20	WT	S2T1	1.8	−0.5	1.8	−0.5	1.1	0.6
SRUPNL	AN20	WT	S2T2	−0.5	1.8	1.0	2.8	0.2	0.9
IXPHNSC	AN21	WT	S1T1	2.5	−1.5	3.2	0.0	−0.7	−1.1
PXHYT	AN21	WT	S1T2	0.3	1.8	−0.4	−0.1	1.5	−0.1
KIJXSC	AN21	WT	S2T1	2.8	−0.5	2.7	0.4	0.4	1.1
UMNSEO	AN21	WT	S2T2	−1.6	−0.4	1.1	2.5	1.7	0.7
HGPOL	AN14	Tau	S1T1	1.7	−0.1	1.1	−1.4	1.4	−0.3
GEDMHCB	AN15	Tau	S1T1	3.3	1.7	2.8	1.6	0.5	3.1
DMHCB	AN15	Tau	S2T1	4.2	1.9	3.8	−0.4	0.8	0.7
GEDM	AN15	Tau	S2T2	3.3	2.5	2.9	2.1	1.4	3.1
ABDE	AN24	Tau	S1T1	2.6	3.0	NA	NA	1.3	NA
DEABI	AN24	Tau	S1T2	4.6	3.5	NA	NA	2.6	NA
SGHER	AN25	Tau	S1T1	2.3	1.4	NA	NA	2.0	NA
KMFGHE	AN25	Tau	S1T2	1.0	2.3	NA	NA	1.4	NA

1. See Table 2 for details.

External space contributed to the firing activities of CA1 neurons in Tau mice

Does the lack of location-specificity and experience-dependence mean that external space made no contribution to Tau neurons’ firing activities at all? To answer this, we first examined whether the firing locations of Tau neurons were completely random. Visual inspection of the lap-by-lap spike trains of Tau neurons revealed that the firing locations of Tau neurons were often consistent among a few laps on both the familiar (Figure 2B) and novel (Figure 6B) trajectories, suggesting that the locations were not completely random. Indeed, randomly sliding rate curves of individual laps further significantly reduced the median rate-stability of Tau neurons on both familiar (−0.0020 [−0.034 0.042], p=9.3 × 10⁻⁴⁹ compared with actual rate-stability) and novel (−0.0020 [−0.032 0.049], p=1.8 × 10⁻⁶⁰) trajectories, indicating that spatial locations still modulated Tau neurons’ firing activities.

Second, we examined whether Tau neuron sequences were trajectory-selective. On both the familiar and novel tracks, most animals ran two different trajectories (back and forth between two food wells) in each of the two running sessions. For each template sequence derived on a trajectory in a session, we counted the occurrence of the sequence (in Z-scores) not only on the trajectory itself (Self), but also on the same trajectory (ST) in the other session and on the other trajectory (OT) in the same session. On the familiar track, the mean Z-scores of Self and ST were similar for both WT (Self 2.5 ± 0.3, N = 13 templates; ST 2.3 ± 0.2, N = 13; p=0.53) and Tau (Self 2.3 ± 0.3, N = 8; ST 1.6 ± 0.5, N = 8; p=0.27) sequences, but there was a significant difference between Self and OT for both WT (OT 0.29 ± 0.3, N = 13; p=5.8 × 10⁻⁶) and Tau (OT 0.10 ± 0.2, N = 8; p=6.1 × 10⁻⁵) sequences (Figure 7A). The result shows that Tau sequences, like WT sequences, occurred consistently on same trajectories across different sessions and distinguished between different trajectories within same sessions. On the novel track, whereas the mean Z-scores of Self and ST remain comparable for both WT (Self 2.5 ± 0.2, N = 15; ST 1.7 ± 0.3, N = 15; p=0.06) and Tau (Self 2.4 ± 0.4, N = 8; ST 2.6 ± 0.6, N = 4; p=0.79) sequences, there was a significant difference between Self and OT only for WT (OT −0.26 ± 0.2, N = 15; p=3.0 × 10⁻⁹), but not for Tau (OT 1.9 ± 0.5, N = 8; p=0.51) sequences (Figure 7B), suggesting that Tau sequences, unlike WT sequences, did not distinguish the two trajectories on the novel track. This analysis shows that Tau sequences were originally not trajectory-selective on the novel track, but became so on the familiar track. Therefore, although Tau neuron’s firing activities were not location-specific and Tau sequences were not primarily driven by space, there was still a modulation of Tau neuron’s firing activities by spatial experience and spatial trajectory.

Figure 7

Download asset Open asset

There was no apparent spatial or temporal periodicity in the occurrence of Tau sequences

Since same Tau sequences sometimes occurred multiple times in a single lap (Figure 3A), we asked whether they tended to appear periodically in space or time. For all the sequences matched with a template in a session, we visualized the spatial/temporal gaps between neighboring sequences by a raster plot and computed the spatial/temporal auto-correlogram of the sequences using their locations/times to examine the periodicity (Figure 8). Neighboring WT sequences in track sessions displayed a consistent spatial gap and there were periodic peaks in their spatial auto-correlograms (Figure 8A, left). The spatial period was the same as the track length (∼2 m for the familiar track, ∼1 m for the novel track), which was expected, given the repetitively running of same track trajectories. The periodicity was much less obvious in the temporal domain (Figure 8A, right), because the amount of time that animals spent running and at the food sites varied from lap to lap. In contrast, Tau sequences during track sessions did not show any consistent spatial or temporal gaps in their raster plots and no obvious, periodic peaks were observed in either the spatial or temporal auto-correlograms (Figure 8B). The spatial periodicity at the track length disappeared apparently because Tau sequences were not anchored to track locations. The average auto-correlogram across all sessions confirmed this observation on both the familiar and novel tracks (Figure 8C,D). Using the same analysis, Tau sequences in open box sessions did not show apparent spatial or temporal periodicity either (Figure 8B,E). Therefore, there was no strong evidence for a periodic occurrence of Tau sequences.

Figure 8

Download asset Open asset

The robust firing sequences in Tau mice could not be explained by simple behavioral parameters

We have shown the existence of robust Tau sequences that were not anchored to specific locations and even did not distinguish different environments. Is it possible that these sequences were produced simply by some peculiar behaviors of Tau mice? Track running and free exploration of open boxes are simple behavioral tasks that both WT and Tau mice were able to perform. Figure 9A,B show the accumulative running paths and linearized individual laps of a WT and Tau mouse on the familiar rectangular track. Figure 9C shows the accumulative running paths of the same mice in the familiar open box. As seen in these raw plots, the overall behavior of the WT and Tau mouse was similar. We then quantified the behavior using the following parameters. The first, most straightforward parameter is the running speed during the running of a track trajectory or during an open box session. In addition, for each trajectory of a track, we measured the animal’s performance by the number of laps on the trajectory and quantified the quality of the running by computing the cumulative travel distance per lap, the number of stops (defined as speed <4 cm/s lasting ≥2 s) per lap, and the stopping duration per lap. These parameters were computed for 54 familiar (WT: 22, Tau: 32) and 46 novel (WT: 20, Tau: 26) trajectories and 27 familiar (WT: 11, Tau: 16) and 25 novel (WT: 12, Tau: 13) open box sessions. The results are shown in Figure 9D–H. On familiar trajectories, Tau mice completed less number of laps and ran with a slower speed than WT mice, but their running quality as measured by stopping and travel distance was similar. On novel trajectories, Tau mice behaved similarly as WT mice or even better on one measure (less number of stops per lap). In open boxes, Tau mice in general ran slightly faster than WT mice, but the difference was significant only in the novel open box. Since robust Tau sequences were found on both the familiar and novel tracks and these sequences appeared in both the familiar and novel open boxes, it is unlikely that the running behavior per se was responsible for the differences between WT and Tau sequences.

Figure 9

Download asset Open asset

Theta oscillations were qualitatively similar between WT and Tau mice

Theta (6–12 Hz) oscillation in the CA1 local field potentials (LFPs) is associated with normal place cell activities (Buzsaki, 2002) and is known to organize multiple place cells into firing sequences at theta time scale (O’Keefe and Recce, 1993; Skaggs et al., 1996; Dragoi and Buzsaki, 2006). We next asked whether theta oscillations were altered in Tau mice. As seen in Figure 10A, the raw and filtered LFP traces of a Tau mouse appeared similar to those of a WT mouse, except that the overall magnitude of the Tau mouse’s LFPs was reduced, which is expected because of the massive neurodegeneration. Power spectral density (PSD) analysis of the raw traces showed a clear peak within the theta band in the LFP PSDs of both mice, but with a reduced amplitude in the Tau mouse’s (Figure 10B, left). However, when the PSD values were normalized by the total LFP power, the peak of the Tau mouse’s PSD was comparable with that of the WT mouse (Figure 10B, right), indicating the presence of prominent theta oscillations in the LFPs of the Tau mouse.

Figure 10

Download asset Open asset

We quantified the peak frequency and the absolute and normalized theta power for each LFP trace of a recording session. These parameters were compared among WT, Tau, and mice of two other control genotypes (+/−, −/+, see ‘Materials and methods’), which showed no obvious tau-mediated neurodegeneration in CA1 (Figure 1 and Table 1). There was a slight reduction in theta peak frequency in Tau mice (∼8.5 Hz, compared with ∼9.5 Hz in control mice) during familiar track and open box sessions. This difference became even less obvious in novel sessions (Figure 10C). The absolute theta power was apparently reduced in Tau mice from those of control animals (Figure 10D). However, the normalized theta power in Tau mice was significantly lower than that in control mice only during familiar track sessions, but not during open box sessions or novel track sessions (Figure 10E). We also restricted this analysis on LFPs within the start and end times of detected sequences in WT and Tau mice. The observation was similar (Figure 10F–H). Therefore, although LFPs in Tau mice were overall smaller, prominent theta oscillations were still present and qualitatively similar to those of WT mice, suggesting that abnormal theta oscillation is unlikely the cause of the difference between Tau and WT sequences.

There was a small gender difference in Tau mice

We also considered additional factors that may affect Tau neuron firing activities. First, we analyzed whether there was a gender difference in Tau mice. Rather than the sequence-related parameters, we used the rate-stability on track trajectories for this purpose because it allowed the comparison between male and female mice with sufficient samples (Figure 11A,B). Between those neurons recorded from male and those from female WT mice, there was no significant difference in the mean rate-stability either on familiar (male 0.70 [0.33 0.88], female 0.65 [0.29 0.86], p=0.21) or novel (male 0.54 [0.19 0.83], 0.46 [0.16 0.86], p=0.22) trajectories. Between those from male and female Tau mice, the mean rate-stability was similar on novel trajectories (male 0.10 [0.023 0.26], female 0.10 [0.037 0.31], p=0.51). But on the familiar trajectories, the mean rate-stability of the neurons from female Tau mice was significantly greater than that of male Tau mice (male 0.047 [−0.0035 0.14], female 0.13 [0.031 0.42], p=5.5 × 10⁻¹⁶). For both genders and on both the familiar and novel trajectories, the mean rate-stability of WT neurons was always much greater than that of Tau neurons (p≤1.3 × 10⁻¹⁴). These results indicate that the firing stability of CA1 neurons was severely compromised in Tau mice of both genders, but CA1 neurons in female Tau mice did better on familiar trajectories than those in male Tau mice.

Figure 11

Download asset Open asset

Second, we analyzed an additional 163 neurons recorded from four mice with two other control genotypes (+/−, −/+). We found that they were much more stable than Tau neurons and largely similar to WT neurons. On familiar trajectories (Figure 11C), the median rate-stability values of both +/− and −/+ neurons were not different from that of WT ones (+/−: 0.62 [0.35 0.80]; −/+: 0.63 [0.39 0.85]; p≥0.11 compared with WT). Both were significantly greater than that of Tau neurons (p≤1.5 × 10⁻³⁰). On novel trajectories (Figure 11D), the median rate-stability of −/+ neurons was not different from, but that of +/− was slightly smaller than, that of WT neurons (+/− 0.41 [0.090 0.69], −/+ 0.49 [0.21 0.76], p=0.016 between +/− and WT, p=0.90 between −/+ and WT). Both were significantly greater than the median rate-stability of Tau neurons (p≥1.4 × 10⁻²¹).

We have examined the HP spatial memory code in a tauopathy mouse model, the transgenic rTg4510 mice. Our data show that CA1 neurons in the transgenic mice do not fire at specific locations, but still form robust firing sequences. These sequences are not anchored to precise locations of spatial trajectories and do not even distinguish between two very different spatial environments. The sequences seen on novel trajectories already exist in a prior session of free exploration in an open box. Therefore, CA1 firing sequences in the transgenic mice become so rigid that they no longer primarily respond to spatial environments or spatial experience, and therefore no longer capable of encoding spatial memories.

The lack of both spatial specificity and experience-dependence indicates that external spatial input in Tau mice is incapable of taking control of Tau neurons. Nevertheless, Tau neurons do not fall into disorganized firing patterns, but fire with robust sequences. If not external space, what could be the driven force that assembles Tau neurons into these sequences? It has been recently shown that internal brain activities, those related to memory recall (Gelbard-Sagiv et al., 2008; Pastalkova et al., 2008) and those encoding time (MacDonald et al., 2011; Eichenbaum, 2013), can produce CA1 firing sequences similar to those shown here. Therefore, here we propose that internal activities underlie the rigid firing sequences in the transgenic mice. On the other hand, although the firing activities of CA1 neurons in the transgenic mice were not location-specific, they are still modulated by external space and spatial experience (Figure 7). The existence of this external modulation together with the rigid firing sequences paint a picture of ‘cued false activation’. When the transgenic mice are placed in a space, instead of forming/retrieving the space's memory code, CA1 neurons are cued to activate those internally driven activity patterns irrelevant to the current space. According to this interpretation, the reason why these mice cannot form new spatial memories is because the HP network is dominated by internal brain activities.

This interpretation implies that there exists a direct competition between external and internal inputs in HP. Therefore, our data provide evidence that abnormal external-internal interaction may contribute to memory deficits in neurodegenerative diseases. Previous studies have shown that CA1 firing sequences can occur during immobility (Foster and Wilson, 2006; Diba and Buzsaki, 2007; Davidson et al., 2009; Karlsson and Frank, 2009; Carr et al., 2011) or when the animal is spatially restricted (Pastalkova et al., 2008; MacDonald et al., 2011). These sequences are believed to be internally generated because external spatial input stays stationary in these situations. In our experiments, the animals’ paths between the start and end times of Tau sequences spanned a distance on the tracks (Figures 3A and 6E) and in the open boxes (Figure 5C), clearly showing that the animals were moving when Tau sequences occurred. The sequences also occurred in accompany with prominent theta oscillations (Figure 10A,H). Therefore, the non-spatially driven firing sequences were found even when animals were actively moving through space. In this case, the external spatial input associated with the movement is overtaken by internal input in the control of CA1 neurons’ firing activities. This further extends the recent findings that pre-existing neuronal sequences may influence future memory codes (Gupta et al., 2010; Dragoi and Tonegawa, 2011).

What changes in the neural circuits of Tau mice could produce the abnormal completion between internal and external input? Internal activities could originate in CA3, an area where neurons can drive each other via its extensive recurrent connections (Lisman, 1999; Nakazawa et al., 2002). External sensory input reaches CA1 likely through the direct pathway via the entorhinal cortex. In Tau mice, there is evidence that tau pathology starts earlier in the cortex including the entorhinal cortex than in HP, and within HP, neuronal loss is more prominent in CA1 than in CA3 (Ramsden et al., 2005). It is likely that the external input to CA1 through the entorhinal cortex is weakened by the pathology, but the internal input from CA3 is relatively spared. In the entorhinal cortex, grid cells fire spikes in a spatially periodic fashion (Hafting et al., 2005). In our data, although Tau sequences sometimes occurred repeatedly in a running lap, there was no evidence for either spatial or temporal periodicity in Tau sequences (Figure 8). Also Tau cells’ firing activities were not tied to specific locations on the tracks (Figure 2B) and did not show obvious periodic patterns in the open boxes (Figure 4B). Therefore, Tau sequences are unlikely resulted from periodic firing of entorhinal grid cells. Other potentially relevant changes in Tau mice could include interneurons and theta oscillations, since they greatly shape the firing activities of HP place cells (O’Keefe and Recce, 1993; Skaggs et al., 1996; Buzsaki, 2002; Dragoi and Buzsaki, 2006). In Tau mice, the CamKII gene that induces tau pathology and neurodegeneration is not expressed in interneurons and thus should mainly affect pyramidal neurons in HP (Mayford et al., 1996; Sik et al., 1998; Ramsden et al., 2005). However, interneurons could still die or alter their properties as adaptive changes to the tau pathology and loss of pyramidal neurons. In our datasets, we only recorded 11 putative interneurons from WT mice and 12 from Tau mice. There was a trend that Tau interneurons fired with a lower rate than WT ones (WT: 24.2 [11.7 46.7] Hz, Tau: 14.8 [8.8 27.2] Hz, p=0.05). This rate change could be one of the adaptive changes in Tau mice to keep the overall activity of remaining HP pyramidal neurons at normal level. Regarding theta oscillations, although the medium septum, the structure important for theta generation in HP, could be affected by tau pathology, based on the known expression pattern of CamKII (Odeh et al., 2011), our data show that prominent theta oscillations are still present in the CA1 LFPs of Tau mice. In particular, the proportion of theta power in the total LFP power is in many cases similar between WT and Tau mice (Figure 10E,H). Therefore, theta oscillation unlikely plays a major role in the generation of abnormal Tau sequences. Based on these considerations, we propose that the internal input from CA3, by winning over the external input via the direct pathway from the entorhinal cortex, is the main driving force for producing the rigid Tau sequences. As such, Tau sequences mainly reflect the internal information such as old memories stored in the CA3 recurrent connections.

Our data reveal a subtle but significant difference between male and female Tau mice: The firing stability of CA1 neurons on familiar trajectories in female is greater than that in male mice. This is somewhat unexpected, given a previous report that female tau mice show higher level of abnormally phosphorylated tau and worse performance in the Morris water maze task (MWM) than male mice (Yue et al., 2011). Other previous studies on another tau-related mouse model have found more extensive β-amyloid, but not tau, pathology (Hirata-Fukae et al., 2008) and more severe impairments on MWM in females than in males (Clinton et al., 2007). One possible explanation for the discrepancy is the age of animals used in our study. The sex difference of Tau mice was previously examined at 5.5 months old (Yue et al., 2011). At much older ages (7–9 months) as in our study, tau pathology becomes much more extensive and possibly saturated and thus the sex difference may become less significant (Ramsden et al., 2005). Another possibility is that tau pathology may not only affect spatial memory representation, but also other factors important for MWM. For example, it has been proposed that behavioral testing produces more stress in females than males and thus greater behavioral deficits under pathological conditions (Clinton et al., 2007). In our experiments, the animals had been handled for at least 1 month (for tetrode adjustment and maze training) before recordings began. The mice in our study might have a reduced level of stress, which exposed the subtle advantage of females in the firing stability of CA1 cells. This advantage in females could be compensated by a stronger stress response to MWM, producing an overall greater impairment on task performance.

What pathological features in the transgenic mice are responsible for the observed rigid firing sequences? Our recordings were conducted on the transgenic mice at 7–9 month old, which is a late, mature stage of tau pathology that displays all typical pathological features including hyperphosphorylated tau, tau tangles, and neurodegeneration (Ramsden et al., 2005; Santacruz et al., 2005). Previous experiments show that stopping the production of the transgenic tau is sufficient to halt the memory deficits at the behavioral level (Ramsden et al., 2005; Santacruz et al., 2005), pointing to a role of the hyperphosphorylated tau protein itself. Another possibility is that neurodegeneration reduces the neuron number in HP and cortex and result in a limited capacity for memory storage. Future studies on tauopathy models at various pathological stages may uncover the exact features of tau pathology and/or neurodegeneration that cause the observed abnormalities. Nevertheless, the current study allows us to identify the functional alterations of CA1 neurons at a mature stage of tau pathology and lay the foundation for probing their pathological causes.

Currently, no animal models can fully recapitulate the pathological changes in human AD patients (Ashe and Zahs, 2010). The limitation in the animal model calls for caution in applying the finding to the human disease. However, tau pathology is a key feature of AD (Wenk, 2003; Ashe and Zahs, 2010) and plays a key role in its behavioral phenotypes (Ashe and Zahs, 2010). This model does mimic key features of human tauopathy including its age-dependent progression (Ramsden et al., 2005; Santacruz et al., 2005). The functional changes at the cellular level in HP identified in this animal model, such as the low location-specificity and rigid firing sequences, possibly also occur in the human brain with similar tau pathological features. Interestingly, our interpretation that internally generated activities obstruct the formation of new memories is consistent with two typical memory symptoms seen in AD patients: the inability to form new memories (Carlesimo and Oscar-Berman, 1992; Salmon and Bondi, 2009) and the intrusion of old memories (Butters et al., 1987; De Anna et al., 2008). This illustrates that studying the functional changes in memory circuits of animal models can generate novel insight into the memory symptoms of human neurodegenerative diseases including AD.

1. 1. Ashe KH
   2. Zahs KR

   (2010) Probing the biology of Alzheimer's disease in mice

   Neuron 66:631–645.

   https://doi.org/10.1016/j.neuron.2010.04.031

   • Google Scholar
2. 1. Berger Z
   2. Roder H
   3. Hanna A
   4. Carlson A
   5. Rangachari V
   6. Yue M

   et al. (2007) Accumulation of pathological tau species and memory loss in a conditional model of tauopathy

   J Neurosci 27:3650–3662.

   https://doi.org/10.1523/JNEUROSCI.0587-07.2007

   • Google Scholar
3. 1. Burgess N
   2. O’Keefe J

   (2003) Neural representations in human spatial memory

   Trends Cogn Sci 7:517–519.

   https://doi.org/10.1016/j.tics.2003.10.014

   • Google Scholar
4. 1. Butters N
   2. Granholm E
   3. Salmon DP
   4. Grant I
   5. Wolfe J

   (1987) Episodic and semantic memory: a comparison of amnesic and demented patients

   J Clin Exp Neuropsychol 9:479–497.

   https://doi.org/10.1080/01688638708410764

   • Google Scholar
5. 1. Buzsaki G

   (1989) Two-stage model of memory trace formation: a role for “noisy” brain states

   Neuroscience 31:551–570.

   https://doi.org/10.1016/0306-4522(89)90423-5

   • Google Scholar
6. 1. Buzsaki G

   (2002) Theta oscillations in the hippocampus

   Neuron 33:325–340.

   https://doi.org/10.1016/S0896-6273(02)00586-X

   • Google Scholar
7. 1. Buzsaki G

   (2004) Large-scale recording of neuronal ensembles

   Nat Neurosci 7:446–451.

   https://doi.org/10.1038/nn1233

   • Google Scholar
8. 1. Cacucci F
   2. Yi M
   3. Wills TJ
   4. Chapman P
   5. O’Keefe J

   (2008) Place cell firing correlates with memory deficits and amyloid plaque burden in Tg2576 Alzheimer mouse model

   Proc Natl Acad Sci USA 105:7863–7868.

   https://doi.org/10.1073/pnas.0802908105

   • Google Scholar
9. 1. Carlesimo GA
   2. Oscar-Berman M

   (1992) Memory deficits in Alzheimer's patients: a comprehensive review

   Neuropsychol Rev 3:119–169.

   https://doi.org/10.1007/BF01108841

   • Google Scholar
10. 1. Carr MF
    2. Jadhav SP
    3. Frank LM

    (2011) Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval

    Nat Neurosci 14:147–153.

    https://doi.org/10.1038/nn.2732

    • Google Scholar
11. 1. Chapman PF
    2. White GL
    3. Jones MW
    4. Cooper-Blacketer D
    5. Marshall VJ
    6. Irizarry M

    et al. (1999) Impaired synaptic plasticity and learning in aged amyloid precursor protein transgenic mice

    Nat Neurosci 2:271–276.

    https://doi.org/10.1038/6374

    • Google Scholar
12. 1. Clinton LK
    2. Billings LM
    3. Green KN
    4. Caccamo A
    5. Ngo J
    6. Oddo S

    et al. (2007) Age-dependent sexual dimorphism in cognition and stress response in the 3xTg-AD mice

    Neurobiol Dis 28:76–82.

    https://doi.org/10.1016/j.nbd.2007.06.013

    • Google Scholar
13. 1. Colgin LL
    2. Moser EI
    3. Moser MB

    (2008) Understanding memory through hippocampal remapping

    Trends Neurosci 31:469–477.

    https://doi.org/10.1016/j.tins.2008.06.008

    • Google Scholar
14. 1. Csicsvari J
    2. Hirase H
    3. Mamiya A
    4. Buzsaki G

    (2000) Ensemble patterns of hippocampal CA3-CA1 neurons during sharp wave-associated population events

    Neuron 28:585–594.

    https://doi.org/10.1016/S0896-6273(00)00135-5

    • Google Scholar
15. 1. Davidson TJ
    2. Kloosterman F
    3. Wilson MA

    (2009) Hippocampal replay of extended experience

    Neuron 63:497–507.

    https://doi.org/10.1016/j.neuron.2009.07.027

    • Google Scholar
16. 1. De Anna F
    2. Attali E
    3. Freynet L
    4. Foubert L
    5. Laurent A
    6. Dubois B

    et al. (2008) Intrusions in story recall: when over-learned information interferes with episodic memory recall. Evidence from Alzheimer's disease

    Cortex 44:305–311.

    https://doi.org/10.1016/j.cortex.2006.08.001

    • Google Scholar
17. 1. Diba K
    2. Buzsaki G

    (2007) Forward and reverse hippocampal place-cell sequences during ripples

    Nat Neurosci 10:1241–1242.

    https://doi.org/10.1038/nn1961

    • Google Scholar
18. 1. Dickerson BC
    2. Eichenbaum H

    (2010) The episodic memory system: neurocircuitry and disorders

    Neuropsychopharmacology 35:86–104.

    https://doi.org/10.1038/npp.2009.126

    • Google Scholar
19. 1. Dragoi G
    2. Buzsaki G

    (2006) Temporal encoding of place sequences by hippocampal cell assemblies

    Neuron 50:145–157.

    https://doi.org/10.1016/j.neuron.2006.02.023

    • Google Scholar
20. 1. Dragoi G
    2. Tonegawa S

    (2011) Preplay of future place cell sequences by hippocampal cellular assemblies

    Nature 469:397–401.

    https://doi.org/10.1038/nature09633

    • Google Scholar
21. 1. Eichenbaum H

    (2013) Memory on time

    Trends Cogn Sci 17:81–88.

    https://doi.org/10.1016/j.tics.2012.12.007

    • Google Scholar
22. 1. Ekstrom AD
    2. Kahana MJ
    3. Caplan JB
    4. Fields TA
    5. Isham EA
    6. Newman EL

    et al. (2003) Cellular networks underlying human spatial navigation

    Nature 425:184–188.

    https://doi.org/10.1038/nature01964

    • Google Scholar
23. 1. Foster DJ
    2. Wilson MA

    (2006) Reverse replay of behavioural sequences in hippocampal place cells during the awake state

    Nature 440:680–683.

    https://doi.org/10.1038/nature04587

    • Google Scholar
24. Book

    1. Franklin KBJ
    2. Paxinos G

    (2008)

    The mouse brain in stereotaxic coordinates (3rd Ed)

    Orlando, FL: Academic Press.

    • Google Scholar
25. 1. Gelbard-Sagiv H
    2. Mukamel R
    3. Harel M
    4. Malach R
    5. Fried I

    (2008) Internally generated reactivation of single neurons in human hippocampus during free recall

    Science 322:96–101.

    https://doi.org/10.1126/science.1164685

    • Google Scholar
26. 1. Gupta AS
    2. van der Meer MA
    3. Touretzky DS
    4. Redish AD

    (2010) Hippocampal replay is not a simple function of experience

    Neuron 65:695–705.

    https://doi.org/10.1016/j.neuron.2010.01.034

    • Google Scholar
27. 1. Hafting T
    2. Fyhn M
    3. Molden S
    4. Moser MB
    5. Moser EI

    (2005) Microstructure of a spatial map in the entorhinal cortex

    Nature 436:801–806.

    https://doi.org/10.1038/nature03721

    • Google Scholar
28. 1. Harris KD
    2. Csicsvari J
    3. Hirase H
    4. Dragoi G
    5. Buzsaki G

    (2003) Organization of cell assemblies in the hippocampus

    Nature 424:552–556.

    https://doi.org/10.1038/nature01834

    • Google Scholar
29. 1. Hirata-Fukae C
    2. Li HF
    3. Hoe HS
    4. Gray AJ
    5. Minami SS
    6. Hamada K

    et al. (2008) Females exhibit more extensive amyloid, but not tau, pathology in an Alzheimer transgenic model

    Brain Res 1216:92–103.

    https://doi.org/10.1016/j.brainres.2008.03.079

    • Google Scholar
30. 1. Ji D
    2. Wilson MA

    (2007) Coordinated memory replay in the visual cortex and hippocampus during sleep

    Nat Neurosci 10:100–107.

    https://doi.org/10.1038/nn1825

    • Google Scholar
31. 1. Karlsson MP
    2. Frank LM

    (2009) Awake replay of remote experiences in the hippocampus

    Nat Neurosci 12:913–918.

    https://doi.org/10.1038/nn.2344

    • Google Scholar
32. 1. Lee AK
    2. Wilson MA

    (2002) Memory of sequential experience in the hippocampus during slow wave sleep

    Neuron 36:1183–1194.

    https://doi.org/10.1016/S0896-6273(02)01096-6

    • Google Scholar
33. 1. Leutgeb S
    2. Leutgeb JK
    3. Barnes CA
    4. Moser EI
    5. McNaughton BL
    6. Moser MB

    (2005) Independent codes for spatial and episodic memory in hippocampal neuronal ensembles

    Science 309:619–623.

    https://doi.org/10.1126/science.1114037

    • Google Scholar
34. 1. Lisman JE

    (1999) Relating hippocampal circuitry to function: recall of memory sequences by reciprocal dentate-CA3 interactions

    Neuron 22:233–242.

    https://doi.org/10.1016/S0896-6273(00)81085-5

    • Google Scholar
35. 1. MacDonald CJ
    2. Lepage KQ
    3. Eden UT
    4. Eichenbaum H

    (2011) Hippocampal “time cells” bridge the gap in memory for discontiguous events

    Neuron 71:737–749.

    https://doi.org/10.1016/j.neuron.2011.07.012

    • Google Scholar
36. 1. Mayford M
    2. Bach ME
    3. Huang YY
    4. Wang L
    5. Hawkins RD
    6. Kandel ER

    (1996) Control of memory formation through regulated expression of a CaMKII transgene

    Science 274:1678–1683.

    https://doi.org/10.1126/science.274.5293.1678

    • Google Scholar
37. 1. McHugh TJ
    2. Jones MW
    3. Quinn JJ
    4. Balthasar N
    5. Coppari R
    6. Elmquist JK

    et al. (2007) Dentate gyrus NMDA receptors mediate rapid pattern separation in the hippocampal network

    Science 317:94–99.

    https://doi.org/10.1126/science.1140263

    • Google Scholar
38. 1. Muller RU
    2. Kubie JL

    (1987)

    The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells

    J Neurosci 7:1951–1968.

    • Google Scholar
39. 1. Nadasdy Z
    2. Hirase H
    3. Czurko A
    4. Csicsvari J
    5. Buzsaki G

    (1999)

    Replay and time compression of recurring spike sequences in the hippocampus

    J Neurosci 19:9497–9507.

    • Google Scholar
40. 1. Nakazawa K
    2. Quirk MC
    3. Chitwood RA
    4. Watanabe M
    5. Yeckel MF
    6. Sun LD

    et al. (2002) Requirement for hippocampal CA3 NMDA receptors in associative memory recall

    Science 297:211–218.

    https://doi.org/10.1126/science.1071795

    • Google Scholar
41. 1. Odeh F
    2. Leergaard TB
    3. Boy J
    4. Schmidt T
    5. Riess O
    6. Bjaalie JG

    (2011) Atlas of transgenic Tet-Off Ca2+/calmodulin-dependent protein kinase II and prion protein promoter activity in the mouse brain

    NeuroImage 54:2603–2611.

    https://doi.org/10.1016/j.neuroimage.2010.11.032

    • Google Scholar
42. 1. O’Keefe J
    2. Dostrovsky J

    (1971) The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat

    Brain Res 34:171–175.

    https://doi.org/10.1016/0006-8993(71)90358-1

    • Google Scholar
43. 1. O’Keefe J
    2. Recce ML

    (1993) Phase relationship between hippocampal place units and the EEG theta rhythm

    Hippocampus 3:317–330.

    https://doi.org/10.1002/hipo.450030307

    • Google Scholar
44. 1. Palop JJ
    2. Mucke L

    (2010) Amyloid-beta-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks

    Nat Neurosci 13:812–818.

    https://doi.org/10.1038/nn.2583

    • Google Scholar
45. 1. Pastalkova E
    2. Itskov V
    3. Amarasingham A
    4. Buzsaki G

    (2008) Internally generated cell assembly sequences in the rat hippocampus

    Science 321:1322–1327.

    https://doi.org/10.1126/science.1159775

    • Google Scholar
46. 1. Ramsden M
    2. Kotilinek L
    3. Forster C
    4. Paulson J
    5. McGowan E
    6. SantaCruz K

    et al. (2005) Age-dependent neurofibrillary tangle formation, neuron loss, and memory impairment in a mouse model of human tauopathy (P301L)

    J Neurosci 25:10637–10647.

    https://doi.org/10.1523/JNEUROSCI.3279-05.2005

    • Google Scholar
47. 1. Salmon DP
    2. Bondi MW

    (2009) Neuropsychological assessment of dementia

    Annu Rev Psychol 60:257–282.

    https://doi.org/10.1146/annurev.psych.57.102904.190024

    • Google Scholar
48. 1. Santacruz K
    2. Lewis J
    3. Spires T
    4. Paulson J
    5. Kotilinek L
    6. Ingelsson M

    et al. (2005) Tau suppression in a neurodegenerative mouse model improves memory function

    Science 309:476–481.

    https://doi.org/10.1126/science.1113694

    • Google Scholar
49. 1. Sik A
    2. Hajos N
    3. Gulacsi A
    4. Mody I
    5. Freund TF

    (1998) The absence of a major Ca2+ signaling pathway in GABAergic neurons of the hippocampus

    Proc Natl Acad Sci USA 95:3245–3250.

    https://doi.org/10.1073/pnas.95.6.3245

    • Google Scholar
50. 1. Skaggs WE
    2. McNaughton BL

    (1996) Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience

    Science 271:1870–1873.

    https://doi.org/10.1126/science.271.5257.1870

    • Google Scholar
51. Book

    1. Skaggs WE
    2. McNaughton BL
    3. Gothard KM
    4. Markus EJ

    (1993)

    An information-theoretic approach to deciphering the hippocampal code

    In: Hanson SJ, Cowan JD, Giles CJ, editors. Advances in neural information processing systems. Morgan Kaufmann. pp. 1030–1037.

    • Google Scholar
52. 1. Skaggs WE
    2. McNaughton BL
    3. Wilson MA
    4. Barnes CA

    (1996) Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences

    Hippocampus 6:149–172.

    https://doi.org/10.1002/(SICI)1098-1063(1996)6:2<>1.0.CO;2-Z

    • Google Scholar
53. 1. Wenk GL

    (2003)

    Neuropathologic changes in Alzheimer's disease

    J Clin Psychiatry 64:7–10.

    • Google Scholar
54. 1. Wilson MA
    2. McNaughton BL

    (1993) Dynamics of the hippocampal ensemble code for space

    Science 261:1055–1058.

    https://doi.org/10.1126/science.8351520

    • Google Scholar
55. 1. Wilson MA
    2. McNaughton BL

    (1994) Reactivation of hippocampal ensemble memories during sleep

    Science 265:676–679.

    https://doi.org/10.1126/science.8036517

    • Google Scholar
56. 1. Yu H
    2. Saura CA
    3. Choi SY
    4. Sun LD
    5. Yang X
    6. Handler M

    et al. (2001) APP processing and synaptic plasticity in presenilin-1 conditional knockout mice

    Neuron 31:713–726.

    https://doi.org/10.1016/S0896-6273(01)00417-2

    • Google Scholar
57. 1. Yue M
    2. Hanna A
    3. Wilson J
    4. Roder H
    5. Janus C

    (2011) Sex difference in pathology and memory decline in rTg4510 mouse model of tauopathy

    Neurobiol Aging 32:590–603.

    https://doi.org/10.1016/j.neurobiolaging.2009.04.006

    • Google Scholar

Author details

1. Jingheng Cheng

   Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States

   Contribution

   JC, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

2. Daoyun Ji

   1. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States
   2. Department of Neuroscience, Baylor College of Medicine, Houston, United States

   Contribution

   DJ, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   dji@bcm.edu

   Competing interests

   The authors declare that no competing interests exist.

• 1,626

  views

• 337

  downloads

• 70

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.