In the Epistulae Morales ad Lucilium, Seneca wrote: “There are more things, Lucilius, likely to frighten us than there are to crush us; we suffer more often in imagination than in reality”. This sentence, from one of the key figures of the school of stoicism, describes inner fear within our imagination in the absence of a direct fear-provoking stimulus. Nowadays, even though anxiety and fear correspond to the same theoretical construct in some literature, anxiety differentiates from the latter based on the potential nature of the threat in the absence of an imminent harmful stimulus (Calhoon and Tye, 2015; Davis et al., 2010; Steimer, 2002). Anxiety disorders are becoming more commonly reported: 12-month prevalence estimates on mental disorders show that at least 14% of people in the European Union suffer from anxiety disorders (Wittchen et al., 2011), and around 31% of people in the United States have experienced some type of anxiety disorders in their lifetime (Kessler et al., 1994). Different brain areas play a role in the underlying circuitry of anxiety (Sandford et al., 2000). Stimulations in the brainstem, more precisely in the periaqueductal grey matter or the locus coerulus are specifically involved in the symptomatology of anxiety (Graeff et al., 1993; Redmond and Huang, 1979). Some studies have also shown how the amygdala plays a role in humans suffering from anxiety disorders (Birbaumer et al., 1998; Davidson et al., 1999) or in animal models with generalised fear responses associated with anxiety (Gründemann et al., 2019; Tovote et al., 2015; Wolff et al., 2014). Also, by using the elevated plus maze (EPM) as an anxiety task (Pellow et al., 1985), Tye and colleagues induced anxiolytic effects by targeting projections from the basolateral amygdala (BA) to the central nucleus of the amygdala (Tye et al., 2011). Additional structures are involved in anxiety and include the bed nucleus of the stria terminalis, whose subdivisions are differentially involved in anxiety responses (Duvarci et al., 2009; Kim et al., 2013), and the medial prefrontal cortex (mPFC), which has also been directly linked to anxiety processing in both humans (Rauch et al., 1997) and rodents (Park et al., 2016; Shah and Treit, 2003).

Last but not least, the ventral hippocampus (vH) plays a critical role in anxiety behaviour. Lesions in the vH induce anxiolysis during the exploration of elevated open arenas (e.g. EPM) (Jimenez et al., 2018; Kjelstrup et al., 2002; Padilla-Coreano et al., 2016), or generally in tasks associated with approach-avoidance conflicts (Schumacher et al., 2018). Neurons recorded in the vH showed increased firing in locations with elevated anxiety (Ciocchi et al., 2015; Jimenez et al., 2018). Likewise, projections neurons from and to the vH exhibit anxiety-related activity: amygdala projections to the vH are specifically shaping anxiety-related behaviour during the exploration of an EPM (Felix-Ortiz and Tye, 2014; Pi et al., 2020). Similarly, the reciprocal connection (from vH to BA) is involved in the expression of context-dependent fear memories (Kim and Cho, 2020). Furthermore, information related to anxiety in the vH is directly routed to the mPFC (Ciocchi et al., 2015), and synchronised activity in this monosynaptically connected long-range circuit (Adhikari et al., 2010; Adhikari et al., 2011) is essential for the expression of anxiety behaviour. Motivated by the critical role of the dorsal hippocampus (dH) region in the encoding of spatial information (O’Keefe, 1976; O’Keefe and Nadel, 2011), several studies focusing on the vH have described its involvement in spatial coding (Jung et al., 1994; Poucet et al., 1994). Yet, probably due to its anatomical location and the difficulties to record action potentials of single-unit activity in the vH, the individual neuronal dynamics associated with the changes between anxiogenic and non-anxiogenic states during spatial navigation remain poorly understood.

To study the neuronal dynamics associated with anxiety behaviour in the vH, we simultaneously recorded the activity of individual neurons during the exploration of the EPM, as well as during the exploration of a novel behavioural paradigm, the elevated linear maze (ELM). During the same recording session, we modified the ELM from a non-anxiogenic to an anxiogenic configuration, while recording the activity of the same individual vH neurons. This enabled the investigation of the neuronal dynamics within the vH underlying different anxiety states. We specifically examined the remapping of neuronal activity at the single neuron and population level as animals transitioned from a non-anxiogenic configuration to an anxiogenic one. Collectively, the results of this study show that the neuronal activity in the vH does not simply reflect anxiogenic locations but that it is dynamically modulated by the experience and expectation of anxiety during spatial navigation.

To investigate the neuronal dynamics governing anxiety behaviour in the vH, we recorded the activity of individual neurons while rats explored anxiogenic locations. In addition to the classical EPM, we used a novel ELM, which allowed us to rapidly change the anxiety content of the maze to expose rats to non-anxiogenic or anxiogenic configurations. We found that the neuronal activity of the vH exhibited a uniform spatial representation in the non-anxiogenic configuration of the ELM and that vH neurons displayed direction-dependent spatial firing when shuttling from one end of the ELM to another. When the anxiogenic location was introduced by removing the protective sidewalls from half of the track, the peak firing activity remapped towards the newly introduced anxiogenic location, the spatial properties of the neuronal activity diminished, and direction-dependent firing was homogenised. Of important note, neuronal activity in the closed arm of the ELM predicted the extent of the upcoming exploration in the open arm even before rats entered into the open anxiogenic location.

Much of the anxiety research in freely moving rodents has been relying on the EPM. Using the EPM, it has been shown that: amygdala projections to the vH control the expression of anxiety (Felix-Ortiz and Tye, 2014; Pi et al., 2020); there is an anxiety-associated neuronal activity in the vH routed to the mPFC Ciocchi et al., 2015; and that the vH-prefrontal pathway is critical for anxiety behaviour (Adhikari et al., 2010; Adhikari et al., 2011; Ciocchi et al., 2015). The first part of this manuscript focuses on the activity of neurons recorded in the vH during the exploration of an EPM. We divided the exploration of the EPM into different trajectories. We observed a localised increase in the density of peak firing activity when rats crossed the centre in all the different C-C or C-O trajectories (Figure 1E). This might be explained by the fact that not only the open arms of an EPM are anxiogenic, but also the centre zone (Mendes-Gomes et al., 2011). Unfortunately, it proved difficult to analyse these effects quantitatively, because rats explored the open arms only minimally and sporadically, consistent with the anxiogenic nature of the EPM paradigm.

To overcome this problem, we introduced a novel ELM on which rats were motivated to shuttle from one extremity to the other to receive rewards. The ELM had three different configurations: non-anxiogenic (CC configuration); anxiogenic (CO configuration); and a configuration with new texture and visual cues (CT). Configurations could be quickly switched within a session. At the behavioural level, we observed anxiety-related behaviour during ELM exploration comparable to the ones during EPM exploration (Figure 2D and E). However, the main advantage of the ELM was the possibility to record the neuronal activity of the same vH neurons while rapidly modifying ELM configurations and motivating rats to spend more time in the anxiogenic location. After animals transited from a non-anxiogenic to an anxiogenic configuration, we observed a significant increment in peak firing activities predominately located in the open area. We attributed this recruitment or remapping of the neuronal activity to the anxiogenic location. The neuronal mechanisms during spatial remapping remain largely elusive. Global remapping is a phenomenon observed in the dH when animals move from one environment to a different one (Leutgeb et al., 2005). One could argue that the simple removal of the walls is changing the environment of the ELM and therefore animals could perceive the open arm as a completely novel environment, inducing global remapping. Nevertheless, remapping of neuronal activity in a new environment is expected to be random and independent of a previously explored environment (Gauthier and Tank, 2018; Leutgeb et al., 2005; Schlesiger et al., 2018), even when emotional contexts are introduced (Moita et al., 2004). However, the remapping of neuronal activity observed in vH neurons is not arbitrary as it is most prominent in the anxiogenic location, contrary to a uniformly distributed peak activity in case of a random remapping (Figure 3B). Nonetheless, the opening of walls per se, independent of the anxiogenic experience, could cause changes in vH neuronal activity. Previous work has shown that novel environments elicit the activation of vH neurons to a similar extent as an aversive stimulus (Graham et al., 2021). To show that the remapping of neuronal activity relies on anxiety, we changed the ELM from a CC to CT configuration during the same recording session. In the CT configuration, the previously open location changed to a novel one with a different floor texture and visual patterns in the inner part of the walls, but with protective sidewalls kept all along the track (Figure 2B). As anticipated, these changes equally induced a remapping of neuronal activity in the vH. Yet, in this case, the remapping was distributed over the length of the maze and the novel area did not show an increased number of peak firing activity (Figure 3C). Overrepresentation of behaviourally relevant locations or episodes is not novel in the hippocampal research field. It has been previously demonstrated that hippocampal place cells fire preferentially at reward locations during goal-directed tasks (Dupret et al., 2010; Hok et al., 2007; Hollup et al., 2001; Jin and Lee, 2021) and global remapping is observed in the intermediate hippocampus due to motivational changes (Jin and Lee, 2021). Several experiments have tackled the role of dH neurons in fear conditioning and other aversive tasks, resulting in similar activity patterns to the ones found in this research. Dorsal hippocampal place cells were shown to remap or partially remap in the presence of an aversive stimulus (Kong et al., 2021; Moita et al., 2004; Wu et al., 2017), or remap towards areas of aversive stimuli or threats (Schuette et al., 2020; Wang et al., 2012; Wang et al., 2015), by increasing their activation (Wu et al., 2017) or by synchrony with or reactivation of a subset of basal and BA neurons (Girardeau et al., 2017; Kong et al., 2021). Interestingly, the non-aversive stimulus, presented in the same context as the aversive ones, did not induce remapping in dorsal hippocampal neurons (Moita et al., 2004; Wang et al., 2015), a similar effect to our observations in vH neurons.

Furthermore, the vH is strongly associated with anxiety, and the anxiogenic location represents a highly salient environment for vH-dependent computations (Bannerman et al., 2014). This is supported by our finding that neurons of the vH overrepresent the anxiogenic area. Interestingly, in addition to the global remapping, we also found differences in the rate remapping between the CC-CO and the CC2-CT conformation changes. Rate remapping was significantly more robust in the novel context than in the anxiogenic one, in line with the literature suggesting that novelty induces rate remapping in hippocampal neurons (Kaefer et al., 2019; Leutgeb et al., 2005; Rennó-Costa et al., 2010). Regarding spatial properties of the hippocampal neurons, conflicting effects have been observed with aversive stimuli in the dH. In some cases, spatial information was not significantly different between non-aversive and aversive conditions, but the variance was significantly greater Wu et al., 2017; in other cases, spatial information was higher in non-aversive compared to aversive contexts Wang et al., 2015; while others report either an increase in place field size (Moita et al., 2004) or a decrease (Schuette et al., 2020). One paper reports various changes in the activity of vH neurons when mice are confronted with an aversive odour stimulus. They report a decrease in place field size, remapping of the activity and increase of the firing rate in the presence of the aversive odour (Keinath et al., 2014). Similarly, we also observe remapping of neuronal activity in aversive conditions and, in fact, a tendency, yet no significance, of the neurons to increase their firing rate in the CO configuration compared to the CC (Figure 4—figure supplement 1C). However, in our study, the activity of the neurons in vH increases its coverage, while in Keinath et al., it is reduced (smaller place fields). In the Keinath et al. study, the aversive stimulus was presented on a small area while in our case the source of the anxiogenic stimulus cannot be pinpoint to a specific part of the open arm, which might have contributed to the different observations. In our experiments, the spatial properties of the neurons in the anxiogenic area of the CO configuration are reduced (lower spatial information and higher sparsity) compared to the CC configuration (Figure 4). Overall, all these elements further strengthen the role of vH in the emotional processing of information.

Another relevant observation relates to the directional firing of vH neurons. In the non-anxiogenic configuration of the ELM, the spatial activity of single vH neurons varied depending on the direction of exploration. Similar observations have been made in both the dH (McNaughton et al., 1983) and vH (Royer et al., 2010) for linear mazes, suggesting of a common principle underlying spatial information along the dorso-ventral axis of the hippocampus. With respect to the vH, Royer et al. hypothesised that the differential activity between inbound and outbound trajectories might be caused by the reward delivered at the end of the arm implying some reward-associated value coding. In contrast to Royer et al., we placed rewards at both extremities of the ELM. Although this does not invalidate the view of Royer et al., in our study, the direction-dependent firing was homogenised in the anxiogenic configuration of the ELM, with vH neurons exhibiting similar firing independently of the direction of exploration in the open arm (Figure 5). We attribute the homogenisation of the direction-dependent firing in single vH neurons to the relevance of the anxiogenic location for vH-dependent computations. One cannot rule out that the homogenisation is also a product of the noticeable change in the maze configuration, implying that the closed and open arm are easier to discern for the animal. Therefore, in both directions, the animal receives similar visual inputs and, therefore, activity homogenises. However, our control experiments, in which we introduced a new, but non-anxiogenic environment to the ELM (Figure 5C), also resulted in a clear differentiation of both parts of the ELM in both directions of travel. Still, we failed to observe a homogenisation of the activities.

Furthermore, the modulation of the neuronal activity in the anxiogenic location was not exclusively observed while the rats explored the open area. The CO configuration of the ELM contained protective walls in half of the maze, while the other half was entirely open. The neuronal population activity was a good predictor of the extent of the exploration of the open location, even when rats explored the closed arm before entering to the open location. Indeed, the neuronal activity in the closed arm, of neuronal pairs, was sufficient to infer whether rats would perform proximal or distal explorations of the open arm (Figure 6). Anxiety-related modulation of vH neuronal activity in both the closed and the open locations implies that not only a direct experience of anxiety enhances neuronal activity in the vH, but also its anticipation without a direct confrontation to an anxiety-inducing situation.

Overall, we provided evidence that the neuronal dynamics within the vH are subjected to the experience of anxiety. When an anxiogenic situation was encountered, vH neurons, first, over-represented this location (Figure 3). Second, spatial properties of the neurons, with their main activity in the anxiogenic area, were reduced (Figure 4). Third, vH neuronal activity was tuned to the anxiogenic environment, impairing previous direction-dependent neuronal activity manifesting in the absence of anxiety (Figure 5). Fourth, the neuronal activity of vH neurons reflected and predicted the exploration of an anxiogenic location (Figure 6). Collectively, these results expand our view of vH function by highlighting dynamic and predictive computations during anxiety.

Data is available in Github (https://github.com/Klausberger-Laboratory/Malagon-Vina-Anxiety2022; copy archived at Malagon-Vina, 2023).

1. 1. Adhikari A
   2. Topiwala MA
   3. Gordon JA

   (2010) Synchronized activity between the ventral hippocampus and the medial prefrontal cortex during anxiety

   Neuron 65:257–269.

   https://doi.org/10.1016/j.neuron.2009.12.002

   • PubMed
   • Google Scholar
2. 1. Adhikari A
   2. Topiwala MA
   3. Gordon JA

   (2011) Single units in the medial prefrontal cortex with anxiety-related firing patterns are preferentially influenced by ventral hippocampal activity

   Neuron 71:898–910.

   https://doi.org/10.1016/j.neuron.2011.07.027

   • PubMed
   • Google Scholar
3. 1. Alme CB
   2. Miao C
   3. Jezek K
   4. Treves A
   5. Moser EI
   6. Moser MB

   (2014) Place cells in the hippocampus: eleven maps for eleven rooms

   PNAS 111:18428–18435.

   https://doi.org/10.1073/pnas.1421056111

   • PubMed
   • Google Scholar
4. 1. Bannerman DM
   2. Sprengel R
   3. Sanderson DJ
   4. McHugh SB
   5. Rawlins JNP
   6. Monyer H
   7. Seeburg PH

   (2014) Hippocampal synaptic plasticity, spatial memory and anxiety

   Nature Reviews. Neuroscience 15:181–192.

   https://doi.org/10.1038/nrn3677

   • PubMed
   • Google Scholar
5. 1. Birbaumer N
   2. Grodd W
   3. Diedrich O
   4. Klose U
   5. Erb M
   6. Lotze M
   7. Schneider F
   8. Weiss U
   9. Flor H

   (1998) Fmri reveals amygdala activation to human faces in social phobics

   Neuroreport 9:1223–1226.

   https://doi.org/10.1097/00001756-199804200-00048

   • PubMed
   • Google Scholar
6. 1. Buzsáki G
   2. Horváth Z
   3. Urioste R
   4. Hetke J
   5. Wise K

   (1992) High-frequency network oscillation in the hippocampus

   Science 256:1025–1027.

   https://doi.org/10.1126/science.1589772

   • PubMed
   • Google Scholar
7. 1. Calhoon GG
   2. Tye KM

   (2015) Resolving the neural circuits of anxiety

   Nature Neuroscience 18:1394–1404.

   https://doi.org/10.1038/nn.4101

   • PubMed
   • Google Scholar
8. 1. Ciocchi S
   2. Passecker J
   3. Malagon-Vina H
   4. Mikus N
   5. Klausberger T

   (2015) Brain computation: Selective information routing by ventral hippocampal CA1 projection neurons

   Science 348:560–563.

   https://doi.org/10.1126/science.aaa3245

   • PubMed
   • Google Scholar
9. 1. Csicsvari J
   2. Hirase H
   3. Czurko A
   4. Buzsáki G

   (1998) Reliability and state dependence of pyramidal cell-interneuron synapses in the hippocampus: an ensemble approach in the behaving rat

   Neuron 21:179–189.

   https://doi.org/10.1016/s0896-6273(00)80525-5

   • PubMed
   • Google Scholar
10. 1. Csicsvari J
    2. Hirase H
    3. Czurkó A
    4. Mamiya A
    5. Buzsáki G

    (1999) Fast network oscillations in the hippocampal CA1 region of the behaving rat

    The Journal of Neuroscience 19:RC20.

    https://doi.org/10.1523/JNEUROSCI.19-16-j0001.1999

    • PubMed
    • Google Scholar
11. 1. Czurkó A
    2. Hirase H
    3. Csicsvari J
    4. Buzsáki G

    (1999) Sustained activation of hippocampal pyramidal cells by `` space clamping'' in a running wheel

    The European Journal of Neuroscience 11:344–352.

    https://doi.org/10.1046/j.1460-9568.1999.00446.x

    • PubMed
    • Google Scholar
12. 1. Davidson RJ
    2. Abercrombie H
    3. Nitschke JB
    4. Putnam K

    (1999) Regional brain function, emotion and disorders of emotion

    Current Opinion in Neurobiology 9:228–234.

    https://doi.org/10.1016/s0959-4388(99)80032-4

    • PubMed
    • Google Scholar
13. 1. Davis M
    2. Walker DL
    3. Miles L
    4. Grillon C

    (2010) Phasic vs sustained fear in rats and humans: role of the extended amygdala in fear vs anxiety

    Neuropsychopharmacology 35:105–135.

    https://doi.org/10.1038/npp.2009.109

    • PubMed
    • Google Scholar
14. 1. Dupret D
    2. O’Neill J
    3. Pleydell-Bouverie B
    4. Csicsvari J

    (2010) The reorganization and reactivation of hippocampal maps predict spatial memory performance

    Nature Neuroscience 13:995–1002.

    https://doi.org/10.1038/nn.2599

    • PubMed
    • Google Scholar
15. 1. Duvarci S
    2. Bauer EP
    3. Paré D

    (2009) The bed nucleus of the stria terminalis mediates inter-individual variations in anxiety and fear

    The Journal of Neuroscience 29:10357–10361.

    https://doi.org/10.1523/JNEUROSCI.2119-09.2009

    • PubMed
    • Google Scholar
16. 1. Felix-Ortiz AC
    2. Tye KM

    (2014) Amygdala inputs to the ventral hippocampus bidirectionally modulate social behavior

    The Journal of Neuroscience 34:586–595.

    https://doi.org/10.1523/JNEUROSCI.4257-13.2014

    • PubMed
    • Google Scholar
17. 1. Forro T
    2. Volitaki E
    3. Malagon-Vina H
    4. Klausberger T
    5. Nevian T
    6. Ciocchi S

    (2022) Anxiety-related activity of ventral hippocampal interneurons

    Progress in Neurobiology 219:102368.

    https://doi.org/10.1016/j.pneurobio.2022.102368

    • PubMed
    • Google Scholar
18. 1. Gauthier JL
    2. Tank DW

    (2018) A dedicated population for reward coding in the hippocampus

    Neuron 99:179–193.

    https://doi.org/10.1016/j.neuron.2018.06.008

    • PubMed
    • Google Scholar
19. 1. Girardeau G
    2. Inema I
    3. Buzsáki G

    (2017) Reactivations of emotional memory in the hippocampus-amygdala system during sleep

    Nature Neuroscience 20:1634–1642.

    https://doi.org/10.1038/nn.4637

    • PubMed
    • Google Scholar
20. 1. Graeff FG
    2. Silveira MC
    3. Nogueira RL
    4. Audi EA
    5. Oliveira RM

    (1993) Role of the amygdala and periaqueductal gray in anxiety and panic

    Behavioural Brain Research 58:123–131.

    https://doi.org/10.1016/0166-4328(93)90097-a

    • PubMed
    • Google Scholar
21. 1. Graham J
    2. D’Ambra AF
    3. Jung SJ
    4. Teratani-Ota Y
    5. Vishwakarma N
    6. Venkatesh R
    7. Parigi A
    8. Antzoulatos EG
    9. Fioravante D
    10. Wiltgen BJ

    (2021) High-frequency stimulation of ventral CA1 neurons reduces amygdala activity and inhibits fear

    Frontiers in Behavioral Neuroscience 15:595049.

    https://doi.org/10.3389/fnbeh.2021.595049

    • PubMed
    • Google Scholar
22. 1. Gründemann J
    2. Bitterman Y
    3. Lu T
    4. Krabbe S
    5. Grewe BF
    6. Schnitzer MJ
    7. Lüthi A

    (2019) Amygdala ensembles encode behavioral states

    Science 364:eaav8736.

    https://doi.org/10.1126/science.aav8736

    • PubMed
    • Google Scholar
23. 1. Hazan L
    2. Zugaro M
    3. Buzsáki G

    (2006) Klusters, neuroscope, ndmanager: a free software suite for neurophysiological data processing and visualization

    Journal of Neuroscience Methods 155:207–216.

    https://doi.org/10.1016/j.jneumeth.2006.01.017

    • PubMed
    • Google Scholar
24. 1. Hok V
    2. Lenck-Santini PP
    3. Roux S
    4. Save E
    5. Muller RU
    6. Poucet B

    (2007) Goal-related activity in hippocampal place cells

    Journal of Neuroscience 27:472–482.

    https://doi.org/10.1523/JNEUROSCI.2864-06.2007

    • Google Scholar
25. 1. Hollup SA
    2. Molden S
    3. Donnett JG
    4. Moser MB
    5. Moser EI

    (2001) Accumulation of hippocampal place fields at the goal location in an annular watermaze task

    The Journal of Neuroscience 21:1635–1644.

    https://doi.org/10.1523/JNEUROSCI.21-05-01635.2001

    • PubMed
    • Google Scholar
26. 1. Jimenez JC
    2. Su K
    3. Goldberg AR
    4. Luna VM
    5. Biane JS
    6. Ordek G
    7. Zhou P
    8. Ong SK
    9. Wright MA
    10. Zweifel L
    11. Paninski L
    12. Hen R
    13. Kheirbek MA

    (2018) Anxiety cells in a hippocampal-hypothalamic circuit

    Neuron 97:670–683.

    https://doi.org/10.1016/j.neuron.2018.01.016

    • PubMed
    • Google Scholar
27. 1. Jin SW
    2. Lee I

    (2021) Differential encoding of place value between the dorsal and intermediate hippocampus

    Current Biology 31:3053–3072.

    https://doi.org/10.1016/j.cub.2021.04.073

    • PubMed
    • Google Scholar
28. 1. Jung MW
    2. Wiener SI
    3. McNaughton BL

    (1994) Comparison of spatial firing characteristics of units in dorsal and ventral hippocampus of the rat

    The Journal of Neuroscience 14:7347–7356.

    https://doi.org/10.1523/JNEUROSCI.14-12-07347.1994

    • PubMed
    • Google Scholar
29. 1. Kadir SN
    2. Goodman DFM
    3. Harris KD

    (2014) High-Dimensional cluster analysis with the masked em algorithm

    Neural Computation 26:2379–2394.

    https://doi.org/10.1162/NECO_a_00661

    • PubMed
    • Google Scholar
30. 1. Kaefer K
    2. Malagon-Vina H
    3. Dickerson DD
    4. O’Neill J
    5. Trossbach SV
    6. Korth C
    7. Csicsvari J

    (2019) Disrupted-In-Schizophrenia 1 overexpression disrupts hippocampal coding and oscillatory synchronization

    Hippocampus 29:802–816.

    https://doi.org/10.1002/hipo.23076

    • PubMed
    • Google Scholar
31. 1. Keinath AT
    2. Wang ME
    3. Wann EG
    4. Yuan RK
    5. Dudman JT
    6. Muzzio IA

    (2014) Precise spatial coding is preserved along the longitudinal hippocampal axis

    Hippocampus 24:1533–1548.

    https://doi.org/10.1002/hipo.22333

    • PubMed
    • Google Scholar
32. 1. Kessler RC
    2. McGonagle KA
    3. Zhao S
    4. Nelson CB
    5. Hughes M
    6. Eshleman S
    7. Wittchen HU
    8. Kendler KS

    (1994) Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the united states

    Archives of General Psychiatry 51:8.

    https://doi.org/10.1001/archpsyc.1994.03950010008002

    • PubMed
    • Google Scholar
33. 1. Kim SY
    2. Adhikari A
    3. Lee SY
    4. Marshel JH
    5. Kim CK
    6. Mallory CS
    7. Lo M
    8. Pak S
    9. Mattis J
    10. Lim BK
    11. Malenka RC
    12. Warden MR
    13. Neve R
    14. Tye KM
    15. Deisseroth K

    (2013) Diverging neural pathways assemble a behavioural state from separable features in anxiety

    Nature 496:219–223.

    https://doi.org/10.1038/nature12018

    • PubMed
    • Google Scholar
34. 1. Kim WB
    2. Cho JH

    (2020) Encoding of contextual fear memory in hippocampal-amygdala circuit

    Nature Communications 11:1382.

    https://doi.org/10.1038/s41467-020-15121-2

    • PubMed
    • Google Scholar
35. 1. Kjelstrup KG
    2. Tuvnes FA
    3. Steffenach HA
    4. Murison R
    5. Moser EI
    6. Moser MB

    (2002) Reduced fear expression after lesions of the ventral hippocampus

    PNAS 99:10825–10830.

    https://doi.org/10.1073/pnas.152112399

    • PubMed
    • Google Scholar
36. 1. Kohavi R
    2. John GH

    (1997) Wrappers for feature subset selection

    Artificial Intelligence 97:273–324.

    https://doi.org/10.1016/S0004-3702(97)00043-X

    • Google Scholar
37. 1. Kong MS
    2. Kim EJ
    3. Park S
    4. Zweifel LS
    5. Huh Y
    6. Cho J
    7. Kim JJ

    (2021) “Fearful-place” coding in the amygdala-hippocampal network

    eLife 10:e72040.

    https://doi.org/10.7554/eLife.72040

    • PubMed
    • Google Scholar
38. 1. Latuske P
    2. Kornienko O
    3. Kohler L
    4. Allen K

    (2017) Hippocampal remapping and its entorhinal origin

    Frontiers in Behavioral Neuroscience 11:253.

    https://doi.org/10.3389/fnbeh.2017.00253

    • PubMed
    • Google Scholar
39. 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

    • PubMed
    • Google Scholar
40. Software

    1. Malagon-Vina H

    (2023) Malagon-vina-anxiety2022, version swh:1:rev:95a791fbddf865f1cfd9209cf15507cb029ac40f

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:15ee36098422fd0994132999028dd089d682f382;origin=https://github.com/Klausberger-Laboratory/Malagon-Vina-Anxiety2022;visit=swh:1:snp:48c7e5375f35d76122bac9cb8f0f09812dcba441;anchor=swh:1:rev:95a791fbddf865f1cfd9209cf15507cb029ac40f
41. 1. McNaughton BL
    2. Barnes CA
    3. O’Keefe J

    (1983) The contributions of position, direction, and velocity to single unit activity in the hippocampus of freely-moving rats

    Experimental Brain Research 52:41–49.

    https://doi.org/10.1007/BF00237147

    • PubMed
    • Google Scholar
42. 1. Mendes-Gomes J
    2. Miguel TT
    3. Amaral VCS
    4. Nunes-de-Souza RL

    (2011) Corticosterone does not change open elevated plus maze-induced antinociception in mice

    Hormones and Behavior 60:408–413.

    https://doi.org/10.1016/j.yhbeh.2011.07.004

    • PubMed
    • Google Scholar
43. 1. Moita MAP
    2. Rosis S
    3. Zhou Y
    4. LeDoux JE
    5. Blair HT

    (2004) Putting fear in its place: remapping of hippocampal place cells during fear conditioning

    The Journal of Neuroscience 24:7015–7023.

    https://doi.org/10.1523/JNEUROSCI.5492-03.2004

    • PubMed
    • Google Scholar
44. 1. Muller RU
    2. Kubie JL

    (1989) The firing of hippocampal place cells predicts the future position of freely moving rats

    The Journal of Neuroscience 9:4101–4110.

    https://doi.org/10.1523/JNEUROSCI.09-12-04101.1989

    • PubMed
    • Google Scholar
45. 1. Muller RU
    2. Bostock E
    3. Taube JS
    4. Kubie JL

    (1994) On the directional firing properties of hippocampal place cells

    The Journal of Neuroscience 14:7235–7251.

    https://doi.org/10.1523/JNEUROSCI.14-12-07235.1994

    • PubMed
    • Google Scholar
46. 1. O’Keefe J

    (1976) Place units in the hippocampus of the freely moving rat

    Experimental Neurology 51:78–109.

    https://doi.org/10.1016/0014-4886(76)90055-8

    • PubMed
    • Google Scholar
47. 1. O’Keefe J
    2. Nadel L

    (2011) The cognitive map as a hippocampus

    Behavioral and Brain Sciences 2:520–533.

    https://doi.org/10.1017/S0140525X00064256

    • Google Scholar
48. 1. Padilla-Coreano N
    2. Bolkan SS
    3. Pierce GM
    4. Blackman DR
    5. Hardin WD
    6. Garcia-Garcia AL
    7. Spellman TJ
    8. Gordon JA

    (2016) Direct ventral hippocampal-prefrontal input is required for anxiety-related neural activity and behavior

    Neuron 89:857–866.

    https://doi.org/10.1016/j.neuron.2016.01.011

    • PubMed
    • Google Scholar
49. 1. Park J
    2. Wood J
    3. Bondi C
    4. Del Arco A
    5. Moghaddam B

    (2016) Anxiety evokes hypofrontality and disrupts rule-relevant encoding by dorsomedial prefrontal cortex neurons

    The Journal of Neuroscience 36:3322–3335.

    https://doi.org/10.1523/JNEUROSCI.4250-15.2016

    • PubMed
    • Google Scholar
50. Book

    1. Paxinos G
    2. Watson C

    (2007)

    The Rat Brain in Stereotaxic Coordinates (6th ed)

    Academic Press/Elsevier.

    • Google Scholar
51. 1. Pellow S
    2. Chopin P
    3. File SE
    4. Briley M

    (1985) Validation of open: closed arm entries in an elevated plus-maze as a measure of anxiety in the rat

    Journal of Neuroscience Methods 14:149–167.

    https://doi.org/10.1016/0165-0270(85)90031-7

    • PubMed
    • Google Scholar
52. 1. Pi G
    2. Gao D
    3. Wu D
    4. Wang Y
    5. Lei H
    6. Zeng W
    7. Gao Y
    8. Yu H
    9. Xiong R
    10. Jiang T
    11. Li S
    12. Wang X
    13. Guo J
    14. Zhang S
    15. Yin T
    16. He T
    17. Ke D
    18. Li R
    19. Li H
    20. Liu G
    21. Yang X
    22. Luo MH
    23. Zhang X
    24. Yang Y
    25. Wang JZ

    (2020) Posterior basolateral amygdala to ventral hippocampal CA1 drives approach behaviour to exert an anxiolytic effect

    Nature Communications 11:183.

    https://doi.org/10.1038/s41467-019-13919-3

    • PubMed
    • Google Scholar
53. 1. Poucet B
    2. Thinus-Blanc C
    3. Muller RU

    (1994) Place cells in the ventral hippocampus of rats

    Neuroreport 5:2045–2048.

    https://doi.org/10.1097/00001756-199410270-00014

    • PubMed
    • Google Scholar
54. 1. Rauch SL
    2. Savage CR
    3. Alpert NM
    4. Fischman AJ
    5. Jenike MA

    (1997) The functional neuroanatomy of anxiety: a study of three disorders using positron emission tomography and symptom provocation

    Biological Psychiatry 42:446–452.

    https://doi.org/10.1016/S0006-3223(97)00145-5

    • PubMed
    • Google Scholar
55. 1. Redmond DE
    2. Huang YH

    (1979) Current concepts. II. new evidence for a locus coeruleus-norepinephrine connection with anxiety

    Life Sciences 25:2149–2162.

    https://doi.org/10.1016/0024-3205(79)90087-0

    • PubMed
    • Google Scholar
56. 1. Rennó-Costa C
    2. Lisman JE
    3. Verschure PFMJ

    (2010) The mechanism of rate remapping in the dentate gyrus

    Neuron 68:1051–1058.

    https://doi.org/10.1016/j.neuron.2010.11.024

    • PubMed
    • Google Scholar
57. 1. Royer S
    2. Sirota A
    3. Patel J
    4. Buzsáki G

    (2010) Distinct representations and theta dynamics in dorsal and ventral hippocampus

    The Journal of Neuroscience 30:1777–1787.

    https://doi.org/10.1523/JNEUROSCI.4681-09.2010

    • PubMed
    • Google Scholar
58. 1. Sandford JJ
    2. Argyropoulos SV
    3. Nutt DJ

    (2000) The psychobiology of anxiolytic drugs: Part 1: basic neurobiology

    Pharmacology & Therapeutics 88:197–212.

    https://doi.org/10.1016/s0163-7258(00)00082-6

    • PubMed
    • Google Scholar
59. 1. Schlesiger MI
    2. Boublil BL
    3. Hales JB
    4. Leutgeb JK
    5. Leutgeb S

    (2018) Hippocampal global remapping can occur without input from the medial entorhinal cortex

    Cell Reports 22:3152–3159.

    https://doi.org/10.1016/j.celrep.2018.02.082

    • PubMed
    • Google Scholar
60. 1. Schuette PJ
    2. Reis FMCV
    3. Maesta-Pereira S
    4. Chakerian M
    5. Torossian A
    6. Blair GJ
    7. Wang W
    8. Blair HT
    9. Fanselow MS
    10. Kao JC
    11. Adhikari A

    (2020) Long-Term characterization of hippocampal remapping during contextual fear acquisition and extinction

    The Journal of Neuroscience 40:8329–8342.

    https://doi.org/10.1523/JNEUROSCI.1022-20.2020

    • PubMed
    • Google Scholar
61. 1. Schumacher A
    2. Villaruel FR
    3. Ussling A
    4. Riaz S
    5. Lee ACH
    6. Ito R

    (2018) Ventral hippocampal CA1 and CA3 differentially mediate learned approach-avoidance conflict processing

    Current Biology 28:1318–1324.

    https://doi.org/10.1016/j.cub.2018.03.012

    • PubMed
    • Google Scholar
62. 1. Shah AA
    2. Treit D

    (2003) Excitotoxic lesions of the medial prefrontal cortex attenuate fear responses in the elevated-plus maze, social interaction and shock probe burying tests

    Brain Research 969:183–194.

    https://doi.org/10.1016/s0006-8993(03)02299-6

    • PubMed
    • Google Scholar
63. 1. Sirota A
    2. Montgomery S
    3. Fujisawa S
    4. Isomura Y
    5. Zugaro M
    6. Buzsáki G

    (2008) Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm

    Neuron 60:683–697.

    https://doi.org/10.1016/j.neuron.2008.09.014

    • PubMed
    • Google Scholar
64. 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<149::AID-HIPO6>3.0.CO;2-K

    • PubMed
    • Google Scholar
65. 1. Steimer T

    (2002) The biology of fear- and anxiety-related behaviors

    Dialogues in Clinical Neuroscience 4:231–249.

    https://doi.org/10.31887/DCNS.2002.4.3/tsteimer

    • PubMed
    • Google Scholar
66. 1. Tovote P
    2. Fadok JP
    3. Lüthi A

    (2015) Neuronal circuits for fear and anxiety

    Nature Reviews. Neuroscience 16:317–331.

    https://doi.org/10.1038/nrn3945

    • PubMed
    • Google Scholar
67. 1. Tye KM
    2. Prakash R
    3. Kim SY
    4. Fenno LE
    5. Grosenick L
    6. Zarabi H
    7. Thompson KR
    8. Gradinaru V
    9. Ramakrishnan C
    10. Deisseroth K

    (2011) Amygdala circuitry mediating reversible and bidirectional control of anxiety

    Nature 471:358–362.

    https://doi.org/10.1038/nature09820

    • PubMed
    • Google Scholar
68. 1. Walf AA
    2. Frye CA

    (2007) The use of the elevated plus maze as an assay of anxiety-related behavior in rodents

    Nature Protocols 2:322–328.

    https://doi.org/10.1038/nprot.2007.44

    • PubMed
    • Google Scholar
69. 1. Wang ME
    2. Wann EG
    3. Yuan RK
    4. Ramos Álvarez MM
    5. Stead SM
    6. Muzzio IA

    (2012) Long-Term stabilization of place cell remapping produced by a fearful experience

    The Journal of Neuroscience 32:15802–15814.

    https://doi.org/10.1523/JNEUROSCI.0480-12.2012

    • PubMed
    • Google Scholar
70. 1. Wang ME
    2. Yuan RK
    3. Keinath AT
    4. Ramos Álvarez MM
    5. Muzzio IA

    (2015) Extinction of learned fear induces hippocampal place cell remapping

    The Journal of Neuroscience 35:9122–9136.

    https://doi.org/10.1523/JNEUROSCI.4477-14.2015

    • PubMed
    • Google Scholar
71. 1. Wiener SI
    2. Paul CA
    3. Eichenbaum H

    (1989) Spatial and behavioral correlates of hippocampal neuronal activity

    The Journal of Neuroscience 9:2737–2763.

    https://doi.org/10.1523/JNEUROSCI.09-08-02737.1989

    • PubMed
    • Google Scholar
72. 1. Wittchen HU
    2. Jacobi F
    3. Rehm J
    4. Gustavsson A
    5. Svensson M
    6. Jönsson B
    7. Olesen J
    8. Allgulander C
    9. Alonso J
    10. Faravelli C
    11. Fratiglioni L
    12. Jennum P
    13. Lieb R
    14. Maercker A
    15. van Os J
    16. Preisig M
    17. Salvador-Carulla L
    18. Simon R
    19. Steinhausen H-C

    (2011) The size and burden of mental disorders and other disorders of the brain in Europe 2010

    European Neuropsychopharmacology 21:655–679.

    https://doi.org/10.1016/j.euroneuro.2011.07.018

    • PubMed
    • Google Scholar
73. 1. Wolff SBE
    2. Gründemann J
    3. Tovote P
    4. Krabbe S
    5. Jacobson GA
    6. Müller C
    7. Herry C
    8. Ehrlich I
    9. Friedrich RW
    10. Letzkus JJ
    11. Lüthi A

    (2014) Amygdala interneuron subtypes control fear learning through disinhibition

    Nature 509:453–458.

    https://doi.org/10.1038/nature13258

    • PubMed
    • Google Scholar
74. 1. Wu CT
    2. Haggerty D
    3. Kemere C
    4. Ji D

    (2017) Hippocampal awake replay in fear memory retrieval

    Nature Neuroscience 20:571–580.

    https://doi.org/10.1038/nn.4507

    • PubMed
    • Google Scholar
75. 1. Ylinen A
    2. Bragin A
    3. Nádasdy Z
    4. Jandó G
    5. Szabó I
    6. Sik A
    7. Buzsáki G

    (1995) Sharp wave-associated high-frequency oscillation (200 Hz) in the intact hippocampus: network and intracellular mechanisms

    The Journal of Neuroscience 15:30–46.

    https://doi.org/10.1523/JNEUROSCI.15-01-00030.1995

    • PubMed
    • Google Scholar
76. 1. Zhang S
    2. Schönfeld F
    3. Wiskott L
    4. Manahan-Vaughan D

    (2014) Spatial representations of place cells in darkness are supported by path integration and border information

    Frontiers in Behavioral Neuroscience 8:222.

    https://doi.org/10.3389/fnbeh.2014.00222

    • PubMed
    • Google Scholar

Author details

1. Hugo Malagon-Vina

   Division of Cognitive Neurobiology, Center for Brain Research, Medical University of Vienna, Vienna, Austria

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   hugo.malagonvina@meduniwien.ac.at

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5029-8464

2. Stéphane Ciocchi

   1. Division of Cognitive Neurobiology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
   2. Laboratory of Systems Neuroscience, Department of Physiology, University of Bern, Bern, Switzerland

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Thomas Klausberger

   For correspondence

   stephane.ciocchi@unibe.ch

   Competing interests

   No competing interests declared

3. Thomas Klausberger

   Division of Cognitive Neurobiology, Center for Brain Research, Medical University of Vienna, Vienna, Austria

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Stéphane Ciocchi

   For correspondence

   thomas.klausberger@meduniwien.ac.at

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7269-3158

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