Arg3.1 gene deletion on rhythmic synchronization of hippocampal CA1 neurons during locomotor activity and sleep

PII: DOI: Reference:

S1074-7427(16)30019-3 http://dx.doi.org/10.1016/j.nlm.2016.03.021 YNLME 6423

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Neurobiology of Learning and Memory

Received Date: Revised Date: Accepted Date:

22 September 2015 23 February 2016 28 March 2016

Please cite this article as: Malkki, H.A.I., Mertens, P.E.C., Lankelma, J.V., Vinck, M., van Schalkwijk, F.J., Van Mourik-Donga, L.B., Battaglia, F.P., Mahlke, C., Kuhl, D., Pennartz, C.M.A., Effects of Arc/Arg3.1 gene deletion on rhythmic synchronization of hippocampal CA1 neurons during locomotor activity and sleep, Neurobiology of Learning and Memory (2016), doi: http://dx.doi.org/10.1016/j.nlm.2016.03.021

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Effects of Arc/Arg3.1 gene deletion on rhythmic synchronization of hippocampal CA1 neurons during locomotor activity and sleep Hemi A. I. Malkki1,3), Paul E. C. Mertens1,3), Jan V. Lankelma1,3), Martin Vinck1,3,4), Frank J. van Schalkwijk1,3), Laura B. Van Mourik-Donga1,3), Francesco P. Battaglia1,3,5), Claudia Mahlke2,6), Dietmar Kuhl2) and Cyriel M. A. Pennartz1,3),*

1)

Cognitive & Systems Neuroscience, Swammerdam Institute Center for Neuroscience, University of Amsterdam, 2) Institute for Molecular and Cellular Cognition Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251 Hamburg, Germany, 3) Research Priority Program Brain and Cognition, University of Amsterdam, Postal Box 94216, 1090 GE Amsterdam, the Netherlands *Corresponding author Contact details: E-mail address: [email protected] Postal address: Cognitive and Systems Neuroscience, Swammerdam Institute, Center for Neuroscience, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH, Amsterdam, the Netherlands. 

Current address: Department of Neurobiology, Yale School of Medicine, 333 Cedar St., PO Box 208001, New Haven, CT 06520-8001, U.S.A. 

Current address: Donders Institute, Radboud University Nijmegen, Postbus 9010, 6500 GL, Nijmegen, the Netherlands. 

Current address: NeuroCure Cluster of Excellence, Charité - Universitätsmedizin, Berlin, Germany.

_________________________ Abbreviations: Arc/Arg3.1, activity-regulated cytoskeletal-associated protein/activity regulated gene; ISI, inter-spike interval; LFP, local field potential; NREM, non-rapid eye movement sleep; QW, quiet wakefulness; SWR, sharp-wave ripple 1

Abstract The activity-regulated cytoskeletal-associated protein/activity regulated gene (Arc/Arg3.1) is crucial for long-term synaptic plasticity and memory formation. However, the neurophysiological substrates of memory deficits occurring in the absence of Arc/Arg3.1 are unknown. We compared hippocampal CA1 single-unit and local field potential (LFP) activity in Arc/Arg3.1 knockout and wild-type mice during track running and flanking sleep periods. Locomotor activity, basic firing and spatial coding properties of CA1 cells in knockout mice were not different from wild-type mice. During active behavior, however, knockout animals showed a significantly shifted balance in LFP power, with a relative loss in highfrequency (beta-2 and gamma) bands compared to low-frequency bands. Moreover, during trackrunning, knockout mice showed a decrease in phase locking of spiking activity to LFP oscillations in theta, beta and gamma bands. Sleep architecture in knockout mice was not grossly abnormal. Sharpwave ripples, which have been associated with memory consolidation and replay, showed only minor differences in dynamics and amplitude. Altogether, these findings suggest that Arc/Arg3.1 effects on memory formation are not only manifested at the level of molecular pathways regulating synaptic plasticity, but also at the systems level. The disrupted power balance in theta, beta and gamma rhythmicity and concomitant loss of spike-field phase locking may affect memory encoding during initial storage and memory consolidation stages.

Key words gamma rhythm, immediate early gene, memory, phase locking, theta rhythm, synaptic plasticity

1. Introduction Arc/Arg3.1 is an immediate early gene that is strongly induced by plasticity producing stimulation and, in behaving animals, upon exploration of novel environments (Vazdarjanova et al., 2006). After induction, Arc/Arg3.1 mRNA is rapidly transported into dendrites (Link et al., 1995; Lyford et al., 1995) Arc/Arg3.1 is part of a complex molecular network, and regulates AMPA receptor trafficking, spine morphology and changes in the cytoskeleton (Chowdhury et al., 2006; Messaoudi et al., 2007; Peebles et al., 2010; Rial Verde, Lee-Osbourne, Worley, Malinow, & Cline, 2006). This network has gained interest from a clinical viewpoint, as Arc/Arg3.1 regulates β-amyloid generation (Wegenast-Braun et al., 2009; Wu et al., 2011), which is implicated in Alzheimer disease, and contributes to molecular pathways disrupted in Fragile-X mental retardation (Park et al., 2008; Zalfa et al., 2003). From the perspective of learning and memory, Arc/Arg3.1's multi-faceted function is likely related to its importance for both long-term potentiation (LTP) and depression (LTD) as well as neuronal homeostasis and synaptic scaling (Guzowski et al., 2000; 2

Korb, Wilkinson, Delgado, Lovero, & Finkbeiner, 2013; Messaoudi et al., 2007; Plath et al., 2006; Shepherd et al., 2006). Arc/Arg3.1 knockout (KO) mice show a specific deficit in hippocampal long-term synaptic plasticity, whereas short-term plasticity is present and baseline synaptic signaling appears normal (Plath et al., 2006). Consistent with this, KO animals have intact short-term memory and locomotion behavior under baseline conditions but impaired explicit and implicit long-term memory formation and consolidation (McCurry et al., 2010; Plath et al., 2006; Wang et al., 2006). Similar impairments are seen after injection of Arc/Arg3.1 antisense oligonucleotides into the hippocampus (Guzowski et al., 2000). Moreover, Arc/Arg3.1 antisense injection in the lateral amygdala resulted in an impairment in long-term, but not short-term memory in a Pavlovian fear conditioning task in rats (Ploski et al., 2008). However, the neurophysiological substrates of these various memory deficits remain unknown. We investigated the role of Arc/Arg3.1 in hippocampal spatial coding and the regulation of neural oscillatory activity and synchrony, both during a spatial track-running task and flanking sleep–rest episodes. First we assessed whether Arc/Arg3.1 gene knockout affects spatial information coding by hippocampal area CA1 cells (O'Keefe & Dostrovsky, 1971). Secondly, we studied rhythmic mass synaptic activity in wild-type (WT) and KO mice, focusing in particular on the frequency ranges of theta (6-10 Hz) and gamma rhythms (35-100 Hz), which have been implicated in encoding and storage of spatial memory (Bosman, Lansink, & Pennartz, 2014; Carr, Karlsson, & Frank, 2012; Colgin et al., 2009; Gupta, van der Meer, Touretzky, & Redish, 2012; Montgomery & Buzsaki, 2007; Schomburg et al., 2014). Thirdly, as concerns memory consolidation, there is strong evidence that sharp wave-ripple (SWR) activity in the hippocampus is closely associated with replay of information acquired during preceding behavior (Davidson, Kloosterman, & Wilson, 2009; Kudrimoti, Barnes, & McNaughton, 1999; Lansink, Goltstein, Lankelma, McNaughton, & Pennartz, 2009; Pennartz, Uylings, Barnes, & McNaughton, 2002; Wilson & McNaughton, 1994), and that such activity causally contributes to spatial memory (Ego-Stengel & Wilson, 2010; Girardeau, Benchenane, Wiener, Buzsáki, & Zugaro, 2009; Jadhav, Kemere, German, & Frank, 2012). Considering the long-term memory deficits of Arc/Arg3.1 KO mice, we thus asked whether basic parameters of SWR activity are altered in KO mice.

2. Material and methods 2.1 Mice Arc/Arg3.1 KO and WT mice were generated and bred at the Center for Molecular Neurobiology, University of Hamburg (Germany) as described (Plath et al., 2006). Mice (all male) arrived at the local animal housing facility of the University of Amsterdam at the age of 5–7 weeks. After arrival, mice were habituated to the colony rooms on a day-night cycle with lights off at 9.00 hrs (on at 21.00 hrs) for at least 3 weeks prior to surgery. During the habituation period, mice were offered sucrose pellets (14 mg, Bioserv, Frenchtown, NJ) in addition to the regular food chow in the home cage. Recordings were made during the active phase of the day-night cycle (in between 9.00 - 21.00 hrs). Before implantation, mice were housed in pairs with ad libitum access to food, except during pretraining, when animals were subjected to food restriction prior to training to achieve about 5% weight loss. Water was provided ad libitum in the home cage at all times. Two weeks before surgery, the 3

experimenters started handling the animals and carried out pretraining sessions, during which mice learned to collect sucrose pellets while exploring a T-maze (not used for the recordings described here). Mice that did not learn to reliably explore the maze and consume the sucrose pellets were excluded from further experiments. All experimental procedures were approved by the institution's Animal Welfare Committee and were in compliance with the European Council Directive (86/609/EEC) and Principles of laboratory animal care (NIH publication No. 86-23, revised 1985).

2.2 Behavioral protocol Each session, mice were allowed to run 20 laps unidirectionally on a circular track (inner diameter: 60 cm; track width: 6 cm, see Fig 1). Sucrose pellets (about 10 per track-running episode) were dropped at arbitrary locations on the track. Before and after track running, mice were allowed to rest or sleep for about 30 minutes in their home cage which was placed in the middle of the circular track. Recording sessions were conducted twice per day for three to four days. After recordings, mice had ad libitum access to food in the home cage. Behavior and neuronal activity during track-running as well as preceding and subsequent rest phases were monitored in the same experimental room to maintain consistent electrophysiological recording conditions and minimize environmental differences not under the control of the experimenter. Within this room, two distinct environments were created to assess remapping of CA1 place fields in different environments (Muller & Kubie, 1987). Each session, mice were placed on one of two circular tracks. These circular tracks on which the mice ran were identical in shape and color, but differed in odor, auditory noise, lighting level and surrounding cues, including the round arena around which the circular track was positioned. In a given environment, a particular cue on the recording enclosure was repeated elsewhere on the same enclosure to prevent identification of track location by a single visual cue. Noise was applied via a speaker placed beneath the arena. Furthermore, mice were transported from the colony rooms to experiments using different tracks via different routes. Both environmental conditions were presented once on a given day, each in a separate session. Throughout the full series of recording days, each mouse started on day 1 with two novelty sessions (one for each track) followed by three sessions in a familiar environment. Neither the alternation of circular tracks nor their increasing familiarity across sessions showed significant changes; therefore data from all sessions were pooled.

2.3 Tetrodes, microdrive and surgery All recordings were carried out using a custom-made, light-weight (1.6 g) mouse microdrive with 6 independently moveable tetrodes (the Lantern; Battaglia et al., 2009; fig. 1C). After loading the microdrive, tetrodes, each consisting of four polyimide coated nichrome wires (diameter: 17.8 µm; Kanthal, PalmCoast, FL) twisted together, were gold-plated electrolytically in gold cyanide solution (Select Plating, Meppel, the Netherlands) to achieve an impedance of 600-1000 kΩ per lead. Prior to implantation, mice were given a subcutaneous injection of buprenorphine (3 mg/kg; Temgesic, Schering-Plough, Kenilworth, NJ) for sedation and analgesia. Thirty minutes after injection, anesthesia was induced by 3% isoflurane in 100% oxygen, upon which the animal was placed into a stereotact (David Kopf Instruments, Tujunga, CA). Anesthesia was maintained with 1-2% isoflurane. 4

Body temperature was maintained around 36.5 0C with a thermal pad. Six stainless steel screws were inserted into the exposed skull to support the microdrive. One of the supporting screws placed contralaterally to the implant served as ground. A craniotomy of about 1.5 mm in diameter was made over the right hemisphere, -2.00 mm lateral and 2.00 mm posterior to bregma (Paxinos & Franklin, 2004). After removing the dura mater, the exit grid of the drive was placed on top of the brain. The connection was sealed with silastic elastomer (Kwik-Sil, World Precision Instruments, Berlin, Germany) and the drive was anchored to the supporting screws and skull with dental acrylic. Immediately after surgery, tetrodes were lowered by about 500 µm and then turned down across several days to the CA1 pyramidal cell layer, as indicated by sharp wave-ripple oscillations and pyramidal cells exhibiting complex spiking activity. Only data from tetrodes with negative-going SWR complexes and place-active cells were included in further analysis. After implantation, mice were kept in a cylindrical recovery cage having an elevated ceiling. During electrophysiological recordings, mice were 12-20 weeks of age. There was no significant difference in the average age of KO and WT mice (p = 0.27).

2.4 Data acquisition The Lantern was connected to two 16-channel headstage preamplifiers via two connectors (Omnetics Connectors Corporation, Minneapolis, MN; type: NPD-18-FF-GS, Nano Dual Row Male, 18 contacts). The pre-amplifiers were in turn connected to the amplifiers via a commutator and tether cable (Neuralynx, Bozeman, MT). Spiking activity was referenced to one of the tetrodes which was positioned in the corpus callosum. For LFPs, we used either this reference electrode or the ground screw located in the contralateral hemisphere. For single units, the signal was band-pass filtered between 600-6000 Hz. When the voltage signal exceeded a threshold (selected based on the signal/noise ratio), the spiking activity was sampled at 32 kHz during a 1 ms time window, with an amplifier gain of 5000. Using the same tetrodes on which single units were recorded, local field potentials were sampled continuously at a rate of 2 kHz and band-pass filtered between 1 and 475 Hz. Electrophysiological recordings were complemented with videotracking data acquired with Ethovision XT 5.1 software (Noldus, Wageningen, The Netherlands). These data were synchronized by transistor– transistor logic (TTL) pulses sent from Ethovision to the Neuralynx recording system. Automatically tracked coordinates of the mouse’s body center position were manually inspected and corrected. Position data from Ethovision were visually inspected, corrected and exported to MATLAB for further analysis.

2.5 Histology After closure of each experiment, end positions of tetrodes were marked by a lesion induced by 10 µA current through one lead per tetrode for 10 seconds. Mice were sacrificed the following day with an overdose of sodium pentobarbital (Euthasol; 80 mg/kg, ASTfarma BV, Oudewater, the Netherlands), after which a cardiac perfusion with saline, followed by paraformaldehyde (4%), was carried out. Brains were removed and further fixated in 4% paraformaldehyde for at least a week before slicing them in 40 5

µm coronal sections with a vibratome. Brain slices were mounted on gelatin-coated object glasses and Nissl-stained. Recordings of hippocampal neurons were made from locations between approximately 1.8 mm and 2.3 mm posterior and 1.2 mm and 2.4 mm lateral to bregma (Paxinos & Franklin, 2004). The anteriorposterior or medial-lateral positioning of tetrodes was very similar for WT and KO mice. For reference tetrodes, endpoints of the tetrode tracks were found in the corpus callosum. Endpoints of recording tetrodes were mostly in stratum pyramidale and sometimes in stratum radiatum, approximately 1/3 on the way to stratum lacunosum-moleculare (See Fig. 1D for examples).

2.6 Analysis of behavioral data Locomotion speed was calculated based on the time-stamped location data of the animal as recorded by Ethovision XT. In addition to assessing the locomotion speed per session, these data were used in order to exclude spiking activity during periods of immobility from place field analysis (see below). All results were averaged over 27 (WT) and 19 (KO) track running episodes.

2.7 Analysis of local field potentials and sleep-stage detection For all LFP analyses, high-amplitude artifacts (>2000 µV, with margin of 25 ms) were discarded, and 50 Hz oscillation and its harmonics were removed by notch filtering. For power analysis, we used Fast Fourier Transform (FFT) with the Hamming tapering method (Kalenscher, Lansink, Lankelma, & Pennartz, 2010). This method was also used for estimating the ratio of theta vs. overall LFP power. Spectral power in a given frequency range was normalized by dividing by the total area underneath the linear progression of frequencies from 1 to 250 Hz. Mean LFP power was computed across all tetrodes (except the one left in the corpus callosum for reference, see ‘Data acquisition’) in a given session, and these session means were used to calculate the means per genotype. Sleep-rest recordings were divided into four stages: ‘active awake’, ‘REM sleep’, ‘non-REM sleep/quiet wakefulness’ (NREM/QW) and ‘unclassified’. All episodes during which the mouse was moving with a velocity of > 1 cm/s were classified as ‘active awake’. Velocity was measured from the center point of the mouse’s body, with a moving average of 3 consecutive samples (0.5 s time step between samples). Episodes during which the animal was immobile and had high theta band (6-10 Hz) activity for at least 5 s were classified as REM sleep (theta vs. overall LFP power ratio > 0.25 as measured in the pyramidal cell layer; cf. Buzsaki et al., 2003; Lansink et al., 2009). Immobile episodes devoid of high theta band activity of at least 5 s were classified as NREM/QW. Episodes which did not meet any of the above mentioned criteria were labeled as ‘unclassified’. We note that the fraction of unclassified periods in fig. 5 may seem relatively high, but this can be ascribed to the fact that, during many episodes from the rest-sleep phase, animals showed minor and occasional (but suprathreshold) motion within the minimal 5 s period, likely in relation to the fact that recordings took place during the active phase of the day-night cycle. Because neither WT nor KO mice showed a difference between pre- versus post-task sleep, these two types of rest period were pooled together unless mentioned otherwise. The total duration of rest/sleep periods was 47.0 + 3.8 minutes per session. Results were averaged over 51 (WT)

6

and 37 (KO) sleep episodes. Mann-Whitney's U test was used for testing significance of ripple characteristics and LFP oscillatory activity.

2.8 Ripple detection In general, ripple detection followed the method of (Lansink et al., 2009). First, LFP recordings were bandpass-filtered between 100-300 Hz. Next, the absolute values of the filtered LFP trace were taken and oscillatory events which exceeded an amplitude threshold of 4 standard deviations of the baseline level for a minimum duration of 25 ms were included as ripples in the analysis. To exclude transient high-frequency gamma bursts and chewing artifacts, we only included events which had their highest amplitude in the time segment corresponding to the middle 50% of the event and had their maximum power in the range between 105 and 250 Hz, i.e. exceeded the maximum power in the 100-105 Hz range. Ripples meeting these criteria were assigned start and stop times, and their duration was computed using the threshold crossings. Intra-ripple oscillatory frequency and peak amplitude were computed for each ripple event. The distribution of inter-ripple intervals (fig. 5F) was analyzed for the NREM/QW periods and was normalized to the number of ripple events within those periods. Logarithms of the absolute amplitudes were used for comparing the peak amplitudes of ripples. As for LFP analysis, prerun and post-run sleep episodes were pooled together. Mann-Whitney's U test was used for testing significance of ripple characteristics.

2.9 Spike sorting and single unit analysis Spike data were pre-processed by a custom-made Python script, which uses the waveform of each tetrode lead to compute the first three principal components of the spike waveforms. The resulting 12dimensional vectors, describing each spike, were classified into clusters by KlustaKwik (Harris, Henze, Csicsvari, Hirase, & Buzsaki, 2000). These clusters were manually assessed and corrected using Klusters 1.6.4 (Hazan, Zugaro, & Buzsaki, 2006) running on Kubuntu 11.04, a free open source Ubuntu operating system distribution package, to ensure that each cluster was well isolated from other clusters recorded on the same tetrode. Examples of isolated clusters are shown in Fig. 2A-F. We only included clusters with a minimum of 100 spikes during the track-running episode and a maximum of 0.5% of interspike intervals shorter than 2 ms. Furthermore, we assigned these clusters to putative pyramidal cells or interneurons based on their waveform features, namely, initial slope of valley decay and half-decay time (Lansink, Goltstein, Lankelma, & Pennartz, 2010). Clusters that could not be assigned to either class were excluded from further analysis. Across four wild-type mice, 63 single units were included, and these were distributed as follows: mouse WT-1: 3 cells; WT-2: 22 cells; WT-3: 20 cells; WT-4: 18 cells. For KO mice the total cell count of 94 was distributed as follows: mouse KO-1: 13 cells; KO-2: 30 cells; KO-3: 51 cells. For putative pyramidal cells, rate maps were generated as described in Battaglia et al. (2009). Briefly, the square encompassing the circular track was divided into 100 x 100 position bins (bin size: 7.2 mm) and the mouse’s location was determined for each videoframe to assign the location of the mouse to 7

one of these bins for every time point. Data from epochs during which the animal was moving below a speed of 2 cm/s were excluded from place field analysis. The average firing rate was computed for each bin and Gaussian smoothing was applied to produce rate maps. A place field was classified as an area of at least 20 continuous bins where a cell’s firing rate was at least 30% of its peak firing rate (cf. Cabral, Fouquet, Rondi-Reig, Pennartz, & Battaglia, 2014) for a similar definition used for mouse area CA1 cells). Place fields which were separated by less than 10 bins were merged and counted as a single place field; place fields smaller than 20 bins were excluded. Measures used to quantify sharpness of place-field tuning of spike trains were computed according to (Skaggs, McNaughton, Wilson, & Barnes, 1996). First, we calculated the spatial information per spike, which indicates how many bits of information each spike conveys: N

I   pi i 1

i  log 2 i  

where the environment was divided into non-overlapping spatial bins i=1,...,N (100 x 100 bins, as above for the rate-maps), pi being the probability of bin i being occupied, λi the mean firing rate for bin i and λ the mean firing rate of the cell. The second measure we quantified was sparsity, which indicates the extent to which spatially selective firing stands out relative to the mean firing rate:

Sparsity 

 2



( pi i ) 2

p i

2

i

Sparsity is bounded between 0 and 1, 0 being maximally sparse and 1 meaning firing equally over the track. Finally, we computed the selectivity, which is defined as the maximal firing rate across spatial bins, divided by the mean firing rate. Student’s independent t-test was used for testing significance, unless otherwise specified.

2.10 Spike-field phase locking Phase-locking analysis was carried out using Fieldtrip (Oostenveld, Fries, Maris, & Schoffelen, 2011) and the methods as described in (Vinck, Battaglia, Womelsdorf, & Pennartz, 2012). The data were divided into K non-overlapping segments of 10 s. For each 10 s segment, we filtered out 50 Hz line noise in the LFP signal by subtracting the Discrete Fourier Transform at [48.8, 48.9, …, 50.2] and [149.4, 149.5, …, 150.6] Hz. For every spike, we then computed the phase of spiking relative to the pyramidal layer LFP as follows. For the ith spike in the kth 10 s segment, we centered a Hanning tapered LFP segment around the spike of length 7/f, where f is frequency. Discrete Fourier Transform of this segment then yielded an instantaneous spike-LFP phase  j ,i ( f ) . We then computed the PPC- a measure of phase-locking not biased by the number of spikes - as:

k 1 m k i 1  j 1 sin(k ,i ( f )) sin(m, j ( f ))  cos(k ,i ( f )) cos(m, j ( f )) PPC  K K k 1 m k Nk Nm K

K

Nk

Nm

8

Next we averaged these PPC values across channels. The sum above computes the phase coincidence between all pairs of spikes coming from disjoint pairs of trials (k,m) and thereby strongly reduces the impact of history effects within spike trains and removes the bias in spike-LFP locking that is inherent to the resultant length (van Wingerden et al., 2012; Vinck et al., 2012; Vinck, van Wingerden, Womelsdorf, Fries, & Pennartz, 2010). To compute statistical significance between conditions, we performed multiple comparison-corrected permutation testing according to Westfall & Young (1993). Given Mctr and MKO cells in the control and knockout conditions, we randomly sampled (without replacement) Mctr and MKO cells in the control and knockout condition for each permutation, and computed the random difference in PPC values between control and knockout. This yielded a randomization distribution. We then performed a stepdown multiple comparison-corrected permutation test using the maximum observed PPC difference across frequencies as test statistic (Westfall & Young, 1993), at p < 0.05 criterion. We analyzed phase precession for place cells with a sparsity (Skaggs et al., 1996) of > 0.25. For a given cell, we defined the place field as running from the location of the (smoothed, see Taylor (2008) peak firing density to the locations where the firing density had reached 50% of the peak firing density. We then fit a linear-circular regression model to this data (Fisher & Lee, 1992).

3. Results 3.1 Behavior and basic firing properties WT and KO mice exhibited similar locomotion speeds during track running: 7.29 ± 0.33 cm/s (WT) and 7.62 cm/s ± 0.49 (KO; p = 0.56; all values are mean ± SEM unless otherwise noted). The proportion of time they spent running on the track was also similar (69 ± 3%; WTs vs. 70 ± 5%; KOs; p = 0.93). The mice were also otherwise indistinguishable to the experimenter during recordings, in agreement with previous observations (Plath et al., 2006). Only cells (N = 157; 63 WT cells and 94 KO cells) that fired at least 100 spikes during track running, and could be classified as putative pyramidal cell or interneuron, were included in the analyses presented here. None of the parameters assessed showed a significant change over the course of the sessions, so data from all the sessions were pooled. Mean firing rates of putative pyramidal cells were very similar (WTs 1.36 ± 0.26 Hz; N = 37 cells; KOs 1.39 ± 0.19 Hz; N = 73 cells; p = 0.92; Fig. 2G). Firing rates of putative interneurons showed a difference, which was however not significant (WTs 4.87 ± 1.70 Hz; N = 26 cells; KOs 2.72 ± 0.53 Hz; N = 21 cells; p = 0.29; note that some tetrode endpoints reached stratum radiatum, which may explain the relatively high incidence of putative interneurons). Below we will first describe place-cell behavior and local field potential dynamics during track running, and subsequently focus on resting and sleeping activity.

3.2 Place fields While exploring the environment, putative pyramidal cells from both WT and KO animals showed place fields with variable sizes and discreteness (Fig. 2B and 2D). Comparing WT and KO mice, median place 9

field areas were similar (Fig. 2H; Mann-Whitney's U test, p = 0.78; median values are used here to avoid bias imposed by outliers) and there was no significant difference in the number of place fields per cell in WT and KO mice (means: 2.31 ± 0.27 and 2.11 ± 0.16, respectively; p = 0.51). Likewise, spatial information (WT: 0.63 ± 0.06; KO: 0.63 ± 0.05 bits/spike; p = 0.99), sparsity (WT: 0.54 ± 0.05; KO: 0.55 ± 0.03; p = 0.74) and selectivity (WT: 7.23 ± 0.55; KO: 7.26 ± 0.52; p=0.97) measures were also nearly identical. The stability of place fields across the track-running period was confirmed by comparing rate maps separately computed for the first and second half of this period (Supplementary material, fig. S-1). Thus, the basic spatial coding properties of hippocampal CA1 neurons appear to be unaffected by a constitutive deletion of the Arc/Arg3.1 gene.

3.3 Local field potentials and rhythmic synchronization during behavior We next examined whether synaptic mass dynamics in hippocampal area CA1, as reflected in local field potentials (fig.3A), may be altered as a consequence of the loss of Arc/Arg3.1 gene. From the viewpoint of memory encoding and long-term storage, especially the theta and gamma bands are of great interest (Colgin et al., 2009; Montgomery & Buzsaki, 2007), but also beta-2 activity (20-35 Hz) has attracted attention because of its prominence in mice exposed to novel environments (Berke, Hetrick, Breck, & Greene, 2008). An overview of LFP spectra of WT (N = 27 sessions) and KO (N = 19 sessions) mice during circular track running revealed that WT mice show higher power in several higher frequency bands as normalized to the mean power in the overall frequency range (1-250 Hz; Fig. 3B). Normalized oscillatory activity was significantly weaker in KO mice in the beta-2 (20-35 Hz, p = 0.003; Fig. 3F), low gamma (3545 Hz, p = 0.0008; Fig. 3G) and high gamma ranges (60-100 Hz, p = 0.0005; Fig. 3H; Mann-Whitney's U test was used to test the significance of LFP results). In the lower frequency bands, KO mice expressed a slightly increased power in the delta range (1-4 Hz; Fig. 3D) and WTs showed a trend towards higher power in theta range (6-10 Hz; Fig. 3E), but these differences were not significant (p = 0.07 and p = 0.33, respectively). There was also no significant change in the low beta (10-20 Hz) frequency range (p = 0.16; data not shown). The decrease in relative power across medium to high frequency bands, observed in KO animals, may reflect a decrease in absolute power in these same bands, or an increase in absolute power at lower frequencies. When comparing absolute power values between KO and WT mice, we did find a trend for absolute power in KO mice to be elevated with respect to WT mice. However, regardless of whether one assigns priority to either absolute or relative measures, these data indicate a disrupted power balance of high versus low frequencies in KO mice.

3.4 Spike-field phase locking during behavior To test whether Arc/Arg3.1-related alterations in mass synaptic activity covary with changes in hippocampal output patterns, we examined locking of hippocampal spiking to different oscillatory rhythms. Overall, locking of hippocampal CA1 neurons (including putative pyramidal cells and interneurons) to LFP was weaker in KO as compared to WT animals across multiple frequency ranges (Fig. 4A-B; corrected for multiple comparisons). During running, WTs showed a significantly stronger (p < 0.05) pair-wise phase consistency than KO mice particularly in the theta, beta-2 and high gamma range. When looking at pyramidal cells only, the KOs showed significantly reduced phase locking in the theta 10

and beta-2 range, but now during restricted segments of the gamma range (Fig. 4C). In the interneuron population, the reduced phase locking effect was significant in theta and beta-2 range (Fig. 4D). The overall reduction in spike-field phase locking in KO mice could result from an extremely strong form of theta phase precession, or from a general disorganization of spiking activity relative to LFP oscillations. Therefore, we tested whether WT and KO mice differ in theta phase precession. This phenomenon was only modestly manifested in both groups of mice. Using cell selection criteria described in the Methods, we found theta phase precession to be significant for 4 out of 19 WT cells (p < 0.01, binomial test) and 3 out of 8 KO cells (p < 0.01, binomial test) with an average t-statistic of the regression of -0.89 ± 0.36 (median ± SE median; n.s., Wilcoxon signed rank test) and -1.09 ± 1.04, respectively. The average theta phase difference between beginning and end of the place field amounted to -1.46 ± 1.01 (median ± SE) radians (p = 0.15, Wilcoxon’s signed rank test) for WT and -1.56 ± 1.05 radians for KO (difference between conditions: n.s.). In conclusion, the reduction in spike-field phase locking in KO mice could not be ascribed to an exceptionally strong phase precession.

3.5 Sleep architecture and ripple properties Next, we examined whether WT and KO mice showed differences in the composition of sleep-rest phases flanking the track-running period. WT and KO mice were indistinguishable in the amount of time spent in different sleep-rest stages (active awake; p = 0.16, REM-sleep; p = 0.07, NREM/QW; p = 0.44, unclassified; p = 0.41, Fig. 5A; pre- and post-task sleep episodes were pooled). In terms of LFP dynamics, WT and KO mice showed similar trends during NREM/QW as observed during track running (not shown; cf. fig.3A-C), with KO mice showing a relative loss in higher frequency ranges (beta-2 and gamma) and a relative gain at lower frequencies. Hippocampal SWRs have been previously associated with replay and memory consolidation and may well be a hallmark of offline memory retrieval and storage processes (Carr, Jadhav, & Frank, 2011; Davidson et al., 2009; Kudrimoti et al., 1999; Pennartz et al., 2004). We analyzed characteristics of SWR activity during sleep in WT and KO mice, such as intra-ripple oscillatory frequency, amplitude and duration. On visual inspection, individual ripples looked relatively similar in WT and KO animals (Fig. 5BC). A minor but significant difference in mean ripple peak amplitude was present: peak amplitudes were slightly lower in WTs (66.1 ± 0.5 dB) than in KOs (68.5 ± 0.5 dB; p = 0.023), but it should be noted that subtle variations in tetrode placement could have contributed to this difference. Regarding the dynamic properties of ripples, intra-ripple oscillatory frequency was slightly but significantly lower in WTs (146 ± 3 Hz) than in KOs (161 ± 4 Hz; p = 0.035; Fig. 5D). Ripple duration was slightly longer in WTs (55 ± 1 ms) than KOs (50 ± 2 ms; p = 0.019; not shown). Furthermore, the distribution of inter-ripple intervals, normalized to the number of ripple events, did not differ (Fig. 5F).

4. Discussion The main results of this study can be summarized as follows: (i) during active behavioral periods, Arc/Arg3.1 KO mice showed an altered hippocampal LFP power balance, attenuating higher frequency bands in favor of lower frequencies; (ii) despite intact theta power, KO mice showed a decrease in 11

phase-locking to theta, beta and gamma rhythm during track running. These changes occurred against a background of freely moving behavior and sleep architecture that was not grossly abnormal relative to WT mice. Moreover, basic firing and place-field properties of CA1 neurons, and ripple features, did not markedly differ between WT and KO mice.

4.1 Impaired neural synchrony in theta, beta and gamma bands During locomotion KO mice showed an attenuated power particularly in the gamma and beta-2 range. The strongly reduced phase-locking of CA1 spikes to oscillations in the theta, beta and gamma ranges during track running was accounted for by both putative pyramidal cells and interneurons (Fig. 4). Whereas the loss of phase-locking in beta and gamma bands may be partially ascribed to a loss of power, the loss of theta-band phase-locking cannot be explained in this manner, because theta power did not differ significantly between genotypes (Fig. 3E). On a cautionary note, it is important to control whether the observed differences between WT and KO cells may be attributable to outlier animals. However, the same trends in the LFP power and phase-locking data were observed when computing the genotype means from averages per mouse. The attenuation of gamma synchronization is particularly intriguing given that this type of coherence may serve as a spike-timing and communication mechanism for cell assemblies processing spatial information, and may provide temporal structure for encoding and retrieving episodic memory (Bosman et al., 2014; Cabral, Vinck, et al., 2014; Carr et al., 2012; Jensen & Lisman, 2005; Lisman & Idiart, 1995; Montgomery & Buzsaki, 2007). The impaired spike phase-locking to gamma rhythms in KO mice suggests that the coordination of neuronal activity, within hippocampus and/or across brain areas, may be hampered. In rats, low-frequency gamma activity has been associated with coupling of area CA3 to CA1 whereas high-frequency gamma activity may couple entorhinal cortex layer III to CA1. Low and high gamma may thus facilitate memory retrieval and encoding, respectively (Carr et al., 2012; Colgin et al., 2009; Colgin & Moser, 2010). The differences in theta-band phase-locking further reinforce the notion of KO animals showing a temporal disorganization of hippocampal activity. The lack of a clear theta-band phase-locking might be explained by a general loss of temporal structure or by an extreme form of phase precession. The latter possibility appears unlikely because theta phase precession was generally modest or not significantly demonstrable in both WT and KO mice. In conjunction with the reduction in low and high-frequency gamma activity, which is nested in theta cycles (Bragin et al., 1995; Schomburg et al., 2014), the loss of theta-band phase-locking offers a candidate mechanism for the impaired formation of long-term spatial memory, as reported for these mice performing the Morris water maze task (Plath et al., 2006). That hippocampus-dependent memory can be hampered by an impaired neural synchrony against a background of intact basal firing properties is underscored by similar effects of a cannabinoid receptor CB1 agonist on hippocampal network properties (Robbe et al., 2006). The reduction of beta-2 band synchrony in KO mice is interesting considering previous findings in rodents, linking this frequency band to exploration of novel environments. Berke et al. (2008) found beta-2 power in the mouse hippocampus to peak at early stages of novel environment exploration. Although such early novelty effects were not investigated in detail in our study, we note that, in Berke et 12

al. (2008), animals ran laps immediately after being placed on the track, whereas in our experiments mice slept and rested in the middle of the arena before track running. Such details of experimental design and their potential effects on novelty stress should be taken into account in follow-up studies on beta-band activity in the mouse hippocampus. Regardless of such potential effects, the decrement in beta-band LFP power and phase-locking we observed in KO mice during locomotion and NREM/QW suggests a processing deficit adding to the impairments already noted for theta and gamma bands. Importantly, the differences in neural synchrony between WT and KO animals were detected in juxtaposition to relatively normal SWR characteristics. Previous studies characterized ripples as carrier events for replay of recent experiences, both in hippocampus and some of its target structures (Davidson et al., 2009; Diba & Buzsáki, 2007; Ego-Stengel & Wilson, 2010; Foster & Wilson, 2006; Girardeau et al., 2009; Jadhav et al., 2012; Carien S Lansink et al., 2008; Pennartz et al., 2004). The current findings suggest that the neurophysiological basis of long-term memory deficits, found in Arc/Arg3.1 KO mice, can be linked to neural synchronization changes as quantified here during spatial behavior, but do not permit to speculate that “off-line” processing, for instance during post-behavioral sleep, would be affected as well. Further studies on sequential replay of spike trains would be needed to assess possible changes in off-line processing in more detail.

4.2 Loci and mechanisms underlying altered neural synchrony Mechanistically, it is not yet feasible to pinpoint exact sites in the hippocampal circuitry accounting for the observed changes in LFP synchrony and spike-field phase locking. Given the key position of pyramidal-interneuron circuits in hippocampal theta and gamma oscillations (Buzsaki et al., 2003), the relative attenuations in high-frequency bands of KO mice likely depend on long-lasting Hebbian modifications of pyramidal-pyramidal cells or pyramidal–interneuron connections (Guzowski et al., 2000; Plath et al., 2006), either within the hippocampal circuitry or in upstream neocortical areas including entorhinal cortex (Colgin et al., 2009). Indeed, a disengagement of pyramidal-interneuron microcircuits in KO animals is in line with the strong loss of spike-LFP phase locking (fig. 4). One may object that exploratory behavior induces Arc/Arg3.1 expression only in CaM kinase II-positive principal neurons, and that the probability of having Arc/Arg3.1 expressed in interneurons is deemed low under naturalistic circumstances (Vazdarjanova et al., 2006). However, in the current KO line Arc/Arg3.1 gene function is already deleted during embryogenesis, and maturation of pyramidal-to-interneuron connections may not be subject to normal long-lasting modifications, adversely affecting development of full-blown network activity.

4.3 Conclusions: the Arc/Arg3.1 knockout mouse as a unique model for learning and memory deficits In conclusion, the Arc/Arg3.1 knockout mouse emerges as a unique animal model to study neural substrates of memory processing, as it exhibits normal basal behavioral (cf. McCurry et al., 2010; Plath et al., 2006) and single-cell properties, as well as a sleep architecture that did not strongly deviate from WT mice, but also specific reductions in neural synchrony that can be related to long-term memory deficits previously reported for the same mouse line. Although direct comparisons have not been made, 13

previous studies on NMDA receptor subunit NR-1 and GluA1 knockout mice did report deviations in basic place-field properties such as their size, selectivity and stability (Cabral, Fouquet, et al., 2014; McHugh, Blum, Tsien, Tonegawa, & Wilson, 1996; Resnik, McFarland, Sprengel, Sakmann, & Mehta, 2012), in contrast to Arc/Arg3.1 knockout mice. The recent implication of Arc/Arg3.1 in molecular signalling pathways involved in Fragile-X mental retardation (Park et al., 2008; Zalfa et al., 2003) and Alzheimer’s disease (Wegenast-Braun et al., 2009; Wu et al., 2011) makes it timely to consider the currently reported alterations as pointers to neural mechanisms causing memory loss and intellectual disabilities in these brain disorders.

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5. Figure legends Figure 1. Recording environment and equipment. A) Layout of a circular track with a box of matches as a size reference. The inner diameter of the track was 60 cm. B) Schematic illustration of the recording environment. Mice walked on the outer (light brown) ring of the track. C) Mouse with a microdrive implant (Battaglia et al., 2009). D) Examples of tetrode endpoints in the hippocampus: lesion from a reference tetrode in the ventral corpus callosum (left) and lesion from a recording tetrode in the pyramidal layer (right).

Figure 2. Cluster isolation, rate maps and place fields. A) Examples of isolated clusters from WT mice; X and Y axis indicate different principal components from different leads. B) Examples of rate maps corresponding to the two clusters from WT mice shown in (A). Colour bar indicates firing rate in Hz. C) Examples of isolated clusters from KO mice. D) Examples of rate maps of two cells from KO mice shown in (C). E-F) Average waveforms and inter-spike interval (ISI) histograms of the above clusters. Shaded outlines around average waveforms on different tetrode leads indicate standard deviations. In the Interspike-interval (ISI) histograms, the dotted line indicates a 1 ms time interval. G) Mean firing rates for putative pyramidal cells in WT and KO mice (N = 37 and N = 73, respectively). H) Median place field size. Error bars indicate standard error of the mean.

Figure 3. Local field potential spectra during track running in wild-type and knockout mice. A) Examples of local field potential traces recorded from area CA1 of wildtype (WT; left) and KO mice (right) during track running. The two lower traces represent raw EEG; the two upper traces have been filtered in between 35 to 100 Hz. Note that a non-normalized voltage scale is used here, uniformly for WT and KO mice. B) Normalized power spectra during circular track running in wild-type (WT; dark grey; N=27 sessions) and KO (light grey; N=19 sessions) mice. Solid lines show the spectra averaged across sessions, while bands below and above indicate the 95% confidence intervals. Abscissa indicates the frequency range on a logarithmic scale. The small peak around 50 Hz is a remnant of the original 50 Hz peak. C) As above, but abscissa now indicates the frequency range on a linear scale to illustrate the spectra in the lower frequency range. D) Mean power in delta (1-4 Hz) range. E) Theta (6-10 Hz). F) Beta-2 (20-35 Hz). G) Low gamma (35-45 Hz). H) High gamma (60-100 Hz). Power in the various frequency ranges was normalized to the average power in the whole spectrum (1-250 Hz). ** p < 0.01; *** p < 0.005.

Figure 4. Spike-field phase locking during track-running. A) Examples of spike-to-field locking in WT and KO animals in theta (6-10 Hz), beta-2 (20-35 Hz) and high gamma (60-100 Hz) ranges. Individual spikes are shown above filtered LFP traces. B) Pairwise phase consistency, an unbiased measure of spike-to-field 15

locking, is plotted as a function of LFP frequency. Bands indicate SEMs. Significant differences between WT and KO mice were found in the theta, beta-2 and high gamma frequency bands and are indicated with gray bars (P<0.05). Both putative pyramidal cells and interneurons were included in this graph (WT: N=63; KO: N=94 cells). C) Pairwise phase consistency for putative pyramidal cells only (WT: N=37, KO: N=73 cells). Significant differences between WT and KO mice were found in the theta, beta-2 range, around 65-70 Hz and 95-100 Hz. D) Pairwise phase consistency for putative interneurons only (WT: N=26 cells; KO: N=21 cells). Significant differences between WT and KO were found in the theta and beta-2 range.

Figure 5. Sleep architecture, ripple characteristics and pairwise correlations. A) The sleep architecture in Arc/Arg3.1 KO (light grey) mice does not grossly deviate from WT animals (dark grey). Values are means ± SEM of the fraction of the total rest-sleep period (duration: 47.0 + 3.8 minutes per session, mean + SEM), comprising both pre- and post-task sleep. B) Real-time LFP traces filtered to 100-300 Hz in WT (upper trace) and KO (lower trace) show ripple activity. C) Real-time samples of ripples (lower sample: WT; upper sample: KO). D) Intra-ripple oscillatory frequency, averaged across sessions (N = 51 for WT mice, N = 37 for KO mice). E) Peak amplitude of ripples (* p < 0.05). F) Relative rate of ripple intervals occurring in time bins plotted on the abscissa in KO (light grey) and WT (dark gray) mice. Inter-ripple intervals were normalized to NREM/QW duration and corrected for number of ripple events. Error bars indicate standard error of the mean. * p < 0.05; Mann-Whitney's U test.

Supplementary material: Figure S-1. Examples of rate maps computed across the first and second half of a track-running period. This figure serves to illustrate the stability of place fields of CA1 neurons in WT and KO mice across the recording of track-running behavior. A) Upper and lower row each represent one example CA1 cell (WT) showing firing activity most dense at one common location on the maze, both in the first and second half of the track-running period. B) Idem for KO mice. The cells in (A) and (B) are the same four examples as illustrated in fig.2. Colour bar indicates firing rate in Hz.

Acknowledgements Part of the data collected in this study was analyzed using the Matlab Fieldtrip toolbox (Oostenveld et al. 2011). This study was funded by NWO grants ALW 820.02.020 and ALW 823.02.020, STW grant 07613 and EU FP7-ICT grant 270108 (to C.M.A.P.) and DFG, SFB 936/project B4 (to D.K.).

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Highlights of the article: “Effects of Arc/Arg3.1 gene deletion on rhythmic synchronization of hippocampal CA1 neurons during locomotor activity and sleep” by Malkki et al. (revised version for Neurobiology of Learning and Memory) 

We studied hippocampal neurophysiology of mice with an Arc/Arg3.1 gene knockout

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Knockout mice showed a relative loss of high-frequency EEG activity in hippocampus

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Knockout mice showed a decrease in phase locking of spikes to EEG oscillations

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Features of place cells, sharp-wave ripples and sleep were not grossly abnormal

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The observed changes may help explain disrupted memory encoding and consolidation

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