Distinct catecholaminergic pathways projecting to hippocampal CA1 transmit contrasting signals during navigation in familiar and novel environments

Neuromodulatory inputs to the hippocampus play pivotal roles in modulating synaptic plasticity, shaping neuronal activity, and influencing learning and memory. Recently it has been shown that the main sources of catecholamines to the hippocampus, ventral tegmental area (VTA) and locus coeruleus (LC), may have overlapping release of neurotransmitters and effects on the hippocampus. Therefore, to dissect the impacts of both VTA and LC circuits on hippocampal function, a thorough examination of how these pathways might differentially operate during behavior and learning is necessary. We therefore utilized 2-photon microscopy to functionally image the activity of VTA and LC axons within the CA1 region of the dorsal hippocampus in head-fixed male mice navigating linear paths within virtual reality (VR) environments. We found that within familiar environments some VTA axons and the vast majority of LC axons showed a correlation with the animals’ running speed. However, as mice approached previously learned rewarded locations, a large majority of VTA axons exhibited a gradual ramping-up of activity, peaking at the reward location. In contrast, LC axons displayed a pre-movement signal predictive of the animal’s transition from immobility to movement. Interestingly, a marked divergence emerged following a switch from the familiar to novel VR environments. Many LC axons showed large increases in activity that remained elevated for over a minute, while the previously observed VTA axon ramping-to-reward dynamics disappeared during the same period. In conclusion, these findings highlight distinct roles of VTA and LC catecholaminergic inputs in the dorsal CA1 hippocampal region. These inputs encode unique information, with reward information in VTA inputs and novelty and kinematic information in LC inputs, likely contributing to differential modulation of hippocampal activity during behavior and learning.

VTA DA inputs to dorsal CA1 of the hippocampus mainly innervate stratum oriens (Takeuchi et al., 2016;Adeniyi et al., 2020;Adeyelu and Ogundele, 2023), and their activity bidirectionally modulates Scha er Collateral (CA3-CA1) synapses (Rosen et al., 2015), enhances persistence of reward-location associations (McNamara et al., 2014), and drives place preference (Mamad et al., 2017).VTA-hippocampus input activity can also bias place eld location (Mamad et al., 2017), improve place eld stability across days (McNamara et al., 2014), and drive reward expectation dependent enhancement of place eld quality (Krishnan et al., 2022).However, many of the e ects of DA modulation of the hippocampus have now been attributed to LC inputs as their activity enhances the strength of Scha er Collateral synapses (Takeuchi et al., 2016), improves memory retention (Kempadoo et al., 2016), improves place eld stability across days (Wagatsuma et al., 2018), and can bias place elds to a location when paired with a reward (Kaufman et al., 2020) through DA mechanisms.Although many of the e ects of LC and VTA are overlapping, potentially indicating shared mechanisms of action, they are believed to play di erent roles in spatial learning and memory (Duszkiewicz et al., 2019).LC inputs in uence the encoding of novel environments (Kempadoo et al., 2016;Wagatsuma et al., 2018), while VTA DA inputs increase persistence of reward context associations (McNamara et al., 2014), alter hippocampal neurons ring rate (Adeniyi et al., 2020), and their suppression can evoke place avoidance (Mamad et al., 2017).It is possible that these di erences arise because of the di erences in activity observed between LC and VTA DA neurons.
Therefore, characterizing the encoding properties of LC and VTA inputs directly in the hippocampus during navigation and spatial learning would provide important insights into the speci c roles of these distinct neuromodulatory pathways.
Additionally, recent ndings indicate considerable heterogeneity in the activity of VTA (Engelhard et al., 2019) and LC (Uematsu et al., 2017;Noei et al., 2022;Chandler et al., 2014) neurons, highlighting the need for projection speci c recordings.Therefore, we functionally imaged VTA DA and LC axons with single axon resolution in dCA1 of mice as they navigated familiar and novel virtual reality (VR) environments for rewards.We observed distinct encoding properties between these sets of inputs during navigation and in response to environmental novelty.During the approach to a previously rewarded location, the activity of most VTA DA axons ramped up.In contrast, most LC axons did not show this ramping activity and instead predicted the start of motion.Additionally, a majority of LC axons and some VTA axons showed activity associated with the animal's velocity.Following exposure to a novel environment, VTA axon ramping-to-reward signals greatly reduced but LC axon activity sharply increased.These ndings support distinct roles for VTA and LC inputs to the hippocampus in spatial navigation of rewarded and novel environments.

Results
To record the activity of dopaminergic inputs to the dorsal hippocampus, we expressed axon-GCaMP6s or axon-GCaMP7b in LC or VTA neurons of di erent mice.We utilized the NET-cre mouse line (Wagatsuma et al., 2018) to restrict expression to noradrenergic LC neurons, and the DAT-Cre line (Zhuang et al., 2005) to restrict expression to dopaminergic VTA neurons (Fig. 1B).Mice were then head-xed and trained to run a linear virtual reality (VR) track for water rewards delivered through a stationary waterspout when they reached the end of the virtual track (Fig. 1A).Following reward delivery, mice were teleported to the beginning of the track and allowed to complete another lap.On experiment day mice navigated the familiar, rewarded VR environment for 10 min while 2-photon microscopy was used to image the calcium activity of LC (87 ROIs from 22 imaging sites in 16 mice) or VTA (9 ROIs from 8 imaging sites in 8 mice) axons in the dorsal CA1 (Fig. 1C).
Based on the z-axis depth of the recording planes, and the presence of increased auto uorescence in stratum pyramidal, we determined all 9 VTA axons were in Stratum Oriens, while for LC recordings, 18 sessions (78 axons in 11 mice) occurred in Stratum Oriens and 5 sessions (9 axons in 5 mice) in Stratum Pyramidalis.Example VTA (left, orange) and LC (right, blue) axon calcium activity aligned to the animal's behavior are shown in Fig. 1D.Axons from both brain regions showed periodic activity linked to the animals' exploration of the VR environment.

Distinct activity dynamics in VTA and LC inputs during rewarded navigation of a familiar environment
To examine axon activity further, we rst looked at the mean activity across all axons as a function of normalized track position (Fig. 1E).As previously reported (Krishnan et al., 2022), VTA DA axons increase activity along the track, peaking at the reward location at the end of the track.In contrast, LC input activity remains relatively constant across all positions along the track (Fig. 1Eii).
To examine if this di erence could be due to the lower sample size of VTA axons compared to LC axons, the LC axons were down-sampled to match the VTA sample size (n = 9 ROIs in 8 mice) and the slope and intercepts of the down-sampled data was found.This was repeated 1000 times and did not generate any LC data points that overlap with VTA data demonstrating the di erence in relationship between position and activity was not due to the di erent sample sizes (Fig. 1Eiii).We also examined the position related activity of individual VTA and LC axons and observed a positive relationship between position and activity in 88.9% of VTA ROIs (8/9 in 7 mice) but only 28.7% of LC ROIs (25/87 from 12 sessions in 12 mice) while 42.5% of LC ROIs (37/87 from 16 sessions in 9 mice) had a negative relationship between position and activity (Fig. 1E.iv.).To account for the di erent track lengths between VTA and LC recordings, we also looked at the virtual distance from the rewarded end of the track as well as time from reward (Supp.For a subset of VTA (n = 6) and LC ROIs (n = 26), the reward at the end of the track was removed, and the activity of these axons in the unrewarded condition was examined (Fig. 2A.)While the slope of the VTA population activity across positions signi cantly decreased (Fig. 2B.i.) as expected and previously reported (Krishnan et al., 2022), the LC population activity across positions did not signi cantly change (Fig. 2C.i.).This con rmed that the ramping activity in VTA axons was due to the animal's proximity to an expected reward and this signal was not present in the average activity of LC axons.
Next, we investigated the mean activity of these axons as a function of velocity.The population mean of both VTA and LC axons increased as velocity increased (Fig. 1F).This is consistent with the nding that LC inputs to dCA1 encode velocity (Kaufman et al., 2020) and the nding that some DA VTA neurons encode kinematics (Engelhard et al., 2019).Again, to account for di erences in sample size, we down sampled the LC axons 1000 times and found the slope and y-intercept of each sampling.The overlap of the VTA and LC slopes and intercepts con rms we cannot conclude any di erences in velocity related activity in the VTA and LC axon populations (Fig. 1F.iii.).However, analyzing individual ROIs, we observed a statistically signi cant positive relationship between velocity and activity in the majority, 72.4%, of LC axons (63/87 ROIs from 21 sessions in 13 mice), while only 28.6% (2/9 ROIs in 2 mice) of VTA axons showed a positive relationship with velocity.(Fig. 1F.iv.).
The strong velocity correlated activity in a small subset of VTA DA axons indicates heterogeneity in the activity of these inputs similar to what is observed in VTA DA somas (Engelhard et al., 2019).To determine if the velocity correlated activity in VTA DA axons is confounded by the ramping to reward activity, we examined this activity in the unrewarded condition where the ramping to reward activity is absent.Here, the VTA population activity across all velocities is decreased but the slope, or the relationship between velocity and activity, remains unchanged (Fig. 2B.ii.).This is consistent with a subset of VTA axons encoding velocity information, while the overall decrease in activity is explained by the decrease in reward related activity demonstrated in Fig. 2B.i.However, the relationship between velocity and LC activity is unchanged in the unrewarded environment (Fig. 2C.ii.), indicating condition invariant velocity encoding in LC axons.    .Linear regression analysis (on all data points, not means) shows that the population of VTA ROIs increase activity during approach of the end of the track while the population of LC ROIs have consistent activity throughout all positions.Linear regression, F test, VTA, P < 1e * 21, LC, P < 0.01.iii, The LC data set was resampled 1000x using n = 9 ROIs to match the number of VTA ROIs and the slope and intercept of the regression line were measured each time (blue dots).The VTA slope is steeper than all LC slopes indicating a stronger positive relationship between position and activity for VTA axons.iv, Linear regression of position binned activity of individual VTA (orange diamonds), and LC (blue, circles) ROIs.The majority (8/9) of VTA ROIs show a signi cant positive relationship with position while LC ROIs show both a positive (25/87) and negative (37/87) relationship.F, i, Same example ROIs as (d) binned by velocity.ii, Same data as (d, ii,) binned by velocity.Linear regression shows that the population of VTA and LC ROIs have a signi cant relationship with velocity.Linear regression, F test, VTA, P < 0.05, LC,P < 1e * 68.iii, Resampling shows the VTA slope and intercept is within the resampled LC slopes and intercepts indicating similar relationships with velocity.iv, Linear regression of individual VTA and LC axons shows the majority (63/87) of LC ROIs have a signi cant positive relationship with velocity while only 2 VTA ROIs show this relationship.g, Same example ROIs as (d) aligned to motion onset.ii, Same data as (d, ii,) aligned to motion onset.Linear regression shows that the population of VTA axons have a negative slope prior to motion onset while LC axons have positive slope.Linear regression, F test, VTA, P < 0.01, LC, P < 1e * 65. iii, Resampling shows the VTA slope is negative while all resampled LC slopes are positive.iv, Linear regression of individual VTA and LC ROIs shows the majority (56/87) of LC ROIs have a signi cant positive slope prior to motion onset while the majority (6/9) of VTA ROIs have a negative slope.
Finally, we examined the activity of LC and VTA axons during rest and the transition to move-143 ment.The population of LC axons ramped up in activity during the 2 s leading up to motion onset 144 (Fig. 1G).This is consistent with reports of activity of LC axons in cortical areas (Reimer et al., 2016) 145 showing LC activity prior to motion onset.In contrast, VTA axons show decreasing activity during 146 the 2 s leading up to motion onset (Fig. 1G).This ramping down in VTA axon activity is likely due  Because the DAT-Cre mice were injected with a virus for expression of either axon-GCaMP6s 165 (4 mice) or axon-GCaMP7b (4 mice), we asked whether these two GCaMP variants led to di erent 166 activity dynamics (Supp.Fig. 2).The axon-GCaMP6s (5 ROIs) and axon-GCaMP7b (4 ROIs) both 167 had a positive relationship with position, negative relationship with time to motion initiation, and 168 a neutral relationship with velocity, although the GCaMP6s relationships were stronger (Supp.Fig. 169 2 A, C).Importantly, when we compared only axon-GCaMP6s expressing VTA ROIs with the axon-170 GCaMP6s expressing LC ROIs (Supp.Fig. 3), we saw similar relationships as the comparisons using 171 all VTA ROIs (Fig. 1).during exposure to novel VR environments.Following 10 minutes in the familiar environment, mice were teleported to a novel VR environment of the same track length, with a reward at the same position at the end of the track.Following teleportation, we found the running speed of both DAT-Cre and NET-Cre mice transiently decreased (Fig. 3A) and they spend less time immobile (Fig. 3A), demonstrating mice recognize they are navigating a novel environment.While the velocity quickly recovered for both groups of mice, the freezing ratio, or amount of time spent immobile, never recovered in the rst ten laps in the novel environment for the NET-Cre mice indicating some novelty induced changes in behavior persist.
We aligned VTA and LC axon activity to the switch to the novel environment and investigated changes in activity due to exposure to novelty.To test whether the mean axon activity is signicantly elevated or lowered, we de ned a baseline by generating 1000 shu es of the axon traces across the entire recording sessions, downsampling the shu ed data 1000 times to match the VTA (n = 7) and LC (n = 87) sample sizes, and calculating the mean and 95% CI of the shu ed data.After teleportation, the periodic activity observed in the mean of VTA axons, likely re ecting ramping-toreward signals in each individual axon, disappeared (Fig. 3B).This is evident in the traces of most of the individual VTA ROIs showing a loss of the ramping-to-reward signal (Fig. 3C), and in the VTA population position binned activity showing a signi cant reduction in ramping activity (Supp.Fig. 4).
However, 1 VTA ROI showed an increase in activity immediately following exposure to novelty (Fig. 3C), indicating heterogeneity across VTA axons in CA1 and the lack of a novelty signal on average may be due to a small sample size.
Strikingly, LC axons show a dramatic increase in mean activity that remained elevated for > 1 minute following exposure to the novel environment (Fig. 3B) similar with ndings that LC cell body activity is elevated for minutes following exposure to environmental novelty (Takeuchi et al., 2016).A signi cant increase in activity above baseline activity following the switch to a novel environment can be seen in 36 LC axon ROIs (from 15/22 sessions in 10/16 mice) (Fig 3C).To further characterize this activity, we found the mean population activity for each lap and separately for 10 s time bins leading up to and following exposure to the novel environment.This analysis shows that LC activity is signi cantly elevated above baseline for 6 consecutive laps and approximately 90 seconds following exposure to the novel environment (Fig. 3D).These ndings demonstrate that LC inputs signal environmental novelty, supporting a role for these inputs in novelty encoding in the hippocampus.

LC activity
It is possible that the change in the amount of time the animals spend running versus immobile in the novel environment could explain the increase in LC activity in the novel environment, as LC activity is related to behavior (Fig. 1).For instance, LC axons show elevated activity during motion versus rest (Fig. 1G) .Therefore, an increase in the time spent in motion upon exposure to the novel environment could lead to an increase in LC activity.To account for the di erences in time spent running vs immobile between the two environments, we removed any periods where the mice were immobile to isolate the e ects of novelty from changes in behavior (Fig. 4 B).When only looking at activity during running in both environments, we found that LC axon activity is elevated for 2 laps, or 40 s, in the novel environment (Fig. 4B-E).Additionally, when we look only at activity when the mice were immobile, we see that activity is elevated for 30s in the novel environment (Supp.Fig. 5 B).Together, this indicates that there are two separate components that drive LC axon activity during the initial exposure to the novel environment.One, a shorter purely novelty-induced increase in activity which occurs during the rst 2 laps, or about 40 s, in the novel environment.Two, a behavior-induced increase in LC activity caused by an increase in the percentage of time spent running that extends beyond the increase in the novelty-induced activity for 6 laps or 90 s.
If the short, novelty-induced signal in LC axons is an additional signal riding on top of the behavior correlated signals -position, velocity, and motion onset-we would expect a disruption of these   (bottom, gray).iii, The average running velocity ± s.e.m. of all mice during the transition to a novel VR environment (top).The average freezing ratio of all mice ± s.e.m., calculated as the time spent immobile (velocity < 5cm/s) divided by the total lap time.Each lap was compared to the nal lap in the familiar environment using a one-way ANOVA with Tukey HSD post hoc test (Blue *, NET-Cre mice P < 0.05; Orange *, DAT-Cre mice P < 0.05).b, Mean normalized uorescence of all VTA ROIs (top, n = 7) and LC ROIs (bottom, n = 87) aligned to the switch to the novel environment.To de ne a baseline and 95% CI (gray shaded region), 1000 shu es were created from the calcium traces and down sampled to match the sample size and averaged.This was repeated 1000 times and the mean and 95% CI of this shu ed data was determined for each frame.Red lines indicate periods where two or more consecutive frames passed above the % CI of the shu ed baseline.c, Normalized F _F activity of all VTA ROIs (top) and LC ROIs (bottom) aligned to the switch to the novel VR environment.d, The normalized uorescence of all LC ROIs binned by lap (left) or into 50 frame bins (right).The baseline and 95% CI (gray shaded region) was de ned using the same method as in (b) Red lines indicate bins above the baseline 95% CI. aligned to the switch to the novel environment.To de ne a baseline and 95% CI (gray shaded region), 1000 shu es were created from the calcium traces and down sampled to match the sample size and averaged.This was repeated 1000 times and the mean and 95% CI of this shu ed data was determined for each frame.
behavioral correlations during the initial lap in a novel environment.To test this we examined these behavioral correlations lap-by-lap following exposure to the novel environment.Indeed, the slopes of the position binned, velocity binned, and motion onset aligned data are all signi cantly more negative in the rst lap in the novel environment than the nal laps of the familiar environment (Fig. 4F-H).This is consistent with a decaying novelty signal that peaks at the start of the rst lap and rides on top of these behaviorally-correlated signals.This produces an elevation in activity at positions near the start of the rst lap that is lower at positions near the end of the rst lap, causing a negative slope relationship between position and LC axon activity on the rst lap (Fig. 4F; light green line).Further, low velocities occur at the start of each lap compared to the end of the lap.Because the novelty signal is highest when animals are running slowest, the novelty signal attens the velocity-LC activity relationship (Fig. 4G; light green line).Lastly, rest periods typically occur at the start of the track.Therefore, motion onset encoding on the rst lap in the novel environment occurs when the novelty signal is highest, again, attening the relationship (Fig. 4H; light green line).By the third lap in the novel environment, where the novelty-induced signal is no longer observable, the relationships between LC activity and position is no longer di erent than the relationship in the familiar environment (Fig. 4F, -green).The relationship between LC activity and motion onset is also no longer di erent by the third lap in the novel environment (Fig. 4H).Although the relationship between velocity and LC activity is di erent in the third novel lap than that of the nal familiar laps, by the nal lap in the novel environment it is no longer di erent than the familiar relationship(Fig.4G).Together this demonstrates that the relationships between LC activity and behavior in the novel environment quickly return to those in the familiar environment.Interestingly, the slope is signi cantly increased in the nal lap of the novel environment (Fig. 4F), potentially indicating the development of activity at the novel reward location as has been previously reported (Kaufman et al., 2020).Altogether, examining the lap-by-lap dynamics of the position, velocity, and motion onset activity indicates that environmental novelty induces a sharp increase in LC input activity during the rst two laps in the novel environment while also inducing a change in behavior that leads to increased LC input activity in subsequent laps.
We also examined the activity of a subset of LC axons (50 ROIs from 11 sessions in 9 mice) during the transition from darkness to the familiar VR environment.These axons showed elevated activity for 20s following initial exposure to the familiar environment (Supp.Fig. 5 A).While this activity did not persist as long as the activity following exposure to the novel environment, it indicates these axons may be generally responsive to abrupt and unpredicted changes to the animal's environment.This ts with the proposed role of the LC in arousal, with novelty driving greater arousal than abrupt exposure to a familiar environment.

Discussion
During spatial navigation in a familiar environment, activity of VTA DA inputs to dCA1 were strongly modulated by position relative to reward, ramping up as mice approached the end of the track where reward was located.This activity could be related to the animal's distance or time from the rewarded location.Further experiments should be conducted to distinguish between time and distance such as those conducted in VTA DA soma recordings (Kim et al., 2020).We have previously shown that this activity is dependent on the history of reward delivery and re ects the animals' reward expectation (Krishnan et al., 2022).VTA axons in dCA1 also showed decreasing activity during rest prior to motion onset.However, after removing reward, VTA axons showed no activity prior to motion onset indicating this relationship was driven by reward related activity.This ramping activity cannot be explained by changes in the animals' sensory experience, as the VR environment and waterspout position remained unchanged in the unrewarded condition.Additionally, on the initial laps with no reward, the ramping activity is still present (Krishnan et al., 2022) indicating this activity is not directly related to the delivery of water but is instead caused by the animal's internal state of reward expectation.
On average, VTA axons showed activity modulated by velocity in a familiar rewarded environment.This relationship was largely due to the activity of two VTA axons that were strongly modulated by velocity, suggesting that there is heterogeneity in the population of VTA axons in dCA1.A positive relationship between average VTA axon activity and velocity persisted in the unrewarded condition and in the novel environment, indicating velocity encoding in these inputs that is not a result of reward related activity.This heterogeneity in the encoding across individual VTA axons is consistent with studies demonstrating heterogeneous encoding of behavioral variables in VTA DA cell bodies, including activity related to rewards and kinematics (Engelhard et al., 2019).
Our ndings that VTA DA axons show no novelty induced activity and instead show reduced activity following exposure to a novel environment, is in contrast with several studies showing novelty induced activity in VTA DA cell bodies (Takeuchi et al., 2016;Duszkiewicz et al., 2019;Lisman and Grace, 2005), indicating potential heterogeneity in VTA neurons in response to novelty.It is possible that some VTA DA inputs to dCA1 respond to novel environments, and the small number of axons recorded here are not representative of the whole population.Another possibility is that the lack of a novelty response we observe is due to di erences in experimental design.Here, mice learned to approach a location for reward which has been shown to lead to ramping activity in dopaminergic VTA neurons (Howe et al., 2013;Krishnan et al., 2022;Kim et al., 2020;London et al., 2018;Jeong et al., 2022).Following exposure to novelty, the disappearance of reward related activity could obscure any novelty induced increases in activity.In addition, as noted above, on average we did observe a velocity associated signal in VTA axons.When mice were exposed to the novel environment their velocity initially decreased.This would be expected to reduce the average signal across the VTA axon population relative to the higher velocity in the familiar environment.It is possible that this decrease could somewhat mask a subtle novelty induced signal in VTA axons.
Therefore, additional experiments should be conducted to investigate the heterogeneity of these axons and their activity under di erent experimental conditions during tightly controlled behavior.
LC axons showed no position encoding.Instead, they were modulated by velocity and ramped up in activity prior to motion initiation, consistent with recordings of LC axons by others in dCA1 (Kaufman et al., 2020) and in the cortex (Reimer et al., 2016), respectively.An important question is how is this LC axon activity impacting hippocampal neurons during navigation in a familiar environment?It is possible that LC axons during navigation provide increases in excitability that promotes place cell activity as both dopamine and norepinephrine in the hippocampus can impact cell excitability (Segal et al., 1991;Edelmann andLessmann, 2011, 2018).Additionally, place cells are exible during spatial navigation with new place elds forming in familiar environments (She eld et al., 2017;Dong et al., 2021) and shifting position with time/experience (Dong et al., 2021).Place elds can also shift to follow changing reward (Gauthier and Tank, 2018)and object locations (Bourboulou et al., 2019).Dopamine and norepinephrine have also been shown to impact hippocampal synaptic plasticity (Zhang et al., 2009;Edelmann and Lessmann, 2011;Hagena and Manahan-Vaughan, 2012;Goh and Manahan-Vaughan, 2013).Therfore, LC inputs may promote the plasticity necessary for place cells to exibly adapt to changes in a familiar environment (Kaufman et al., 2020;Redondo and Morris, 2011).In other words, LC inputs could allow the hippocampus to be exible during navigation through their impacts on synaptic plasticity (Takeuchi et al., 2016;Yamasaki and Takeuchi, 2017;Duszkiewicz et al., 2019).
Exposure to environmental novelty leads to an increase in dopamine in the dorsal hippocampus (Ihalainen et al., 1999) and promotes synaptic plasticity (Li et al., 2003;Hagena andManahan-Vaughan, 2012), hippocampal replay (McNamara et al., 2014;Dupret et al., 2010) and memory persistence (Li et al., 2003;Cohen et al., 2017).In our experiment, exposure to a novel environment caused an increase in LC axon activity but not in VTA DA axon activity, supporting ndings that novel experiences induce activity of LC neurons (Takeuchi et al., 2016).As discussed above, the slowing down of animal behavior in the novel environment could have decreased LC axon activity and reduced the magnitude of the novelty signal we detected during running.The novelty signal we report here may therefore be an under estimate of it's magnitude under matched behavioral set-tings.The increased activity of LC neurons in the novel environment could increase hippocampal neuron activity (Wagatsuma et al., 2018), increase e cacy of Scha er Colateral synapses (Takeuchi et al., 2016), stabilize place cells across days (Wagatsuma et al., 2018) and enhance memory persistence (Wagatsuma et al., 2018;Takeuchi et al., 2016;Chowdhury et al., 2022) through dopamine receptor dependent mechanisms.Importantly, while LC inputs to CA1 have been shown to cause an increase in activity (Wagatsuma et al., 2018;Chowdhury et al., 2022), shape over-representation of novel reward locations (Kaufman et al., 2020), and modulate memory linking (Chowdhury et al., 2022), they have not been shown to play a role in the formation of contextual memories (Chowdhury et al., 2022) or stabilization of place cell maps across days (Wagatsuma et al., 2018) in dCA1.
However, it is possible this novelty induced activity in LC inputs to dCA1 impacts the formation of instant place elds observed in novel environments (She eld et al., 2017;Dong et al., 2021).Instant place elds form on the rst lap of a novel environment, right when the LC novelty signal is highest in dCA1, suggesting LC inputs may play a role in their formation or stabilization.Therefore, further experiments should investigate the role of LC axons on dCA1 place elds on a trial-by-trial basis.
While LC neurons have been shown to impact novelty encoding through dopaminergic mechanisms (Wagatsuma et al., 2018;Takeuchi et al., 2016;Chowdhury et al., 2022), this does not exclude the possibility that they also release norepinephrine during exposure to novelty and exploration of a familiar environment.Indeed, hippocampal levels of norepinephrine also increase during exposure to environmental novelty (Lima et al., 2019;Moreno-Castilla et al., 2017), but how this norepinephrine release e ects hippocampal function is not well understood.Additionally, it is not known whether norepinephrine and dopamine are released from the same LC inputs or from distinct sets of LC inputs.Dopamine is in the synthesis pathway of norepinephrine and in LC neurons it is loaded into vesicles where it is then converted to norepinephrine by dopamine -hydroxylase (Cimarusti et al., 1979).It is possible that high levels of activity of LC inputs, like those occurring during exposure to novelty, lead to release of vesicles before dopamine can be converted to norepinephrine thus leading to the release of dopamine under these conditions.However, low levels of LC activation, like those observed during familiar environment exploration, may provide time for dopamine to be converted to norepinephrine and thus lead to the release of norepinephrine from LC terminals under these conditions.Further experiments investigating the dynamics of dopamine conversion and release from LC terminals in the hippocampus should be conducted to test this hypothesis.
Here we show that LC input activity is modulated by velocity, time to motion onset, abrupt exposure to a familiar environment and abrupt exposure to novel environments.Each of these conditions is associated with an increase in arousal, and LC activity has been strongly linked to arousal levels (Aston-Jones and Bloom, 1981;Berridge and Waterhouse, 2003;Carter et al., 2010).Therefore, rather than encoding each of these variables independently, LC inputs are likely encoding the animals arousal level during spatial navigation.It has been shown that attention and arousal levels impact tuning properties in many cortical areas and this is thought to be mediated through LC activity (Shulman et al., 1979;Bouret and Sara, 2002;Martins and Froemke, 2015;Waterhouse and Navarra, 2019).Similarly, changes in the animals' brain state, including changes in attention (Kentros et al., 2004) and engagement (Pettit et al., 2022), alter the tuning properties of place cells.This indicates arousal could impact the function of hippocampal neurons through these LC inputs, either directly or through astrocytes (Peter Rupprecht et al., 2023).
The distinct activity dynamics exhibited by LC and VTA DA axons during spatial navigation of familiar and novel environments underscore their distinct contributions to hippocampal dependent learning and memory processes.Notably, these ndings reinforce the notion that VTA DA inputs play a pivotal role in the ongoing maintenance and updating of associations between expected rewards and the locations that lead to them, while LC axons appear to be integral to the process of encoding memories of entirely new environments and stimuli.
uorescence was then averaged across all laps in each environment to nd the mean position binned activity in the familiar and novel environments.

Velocity binned uorescent activity
To nd the velocity binned uorescent activity for each ROI, the velocity was divided into 1 cm/s bins from 1 to 30 cm/s.Velocities above this 14 cm/s were excluded from gures because not all mice ran faster than 14 cm/s.For each lap, the ROIs average uorescence in each velocity bin was calculated and then averaged across all laps in each environment to nd the velocity binned activity in the familiar and novel environments.

Motion initiation aligned uorescence
Periods where mice were immobile (velocity < 5 cm/s) for at least 1.5s then proceed to run (velocity g 5cm/s) for at least 3s were identi ed.The uorescent activity for ROIs for these periods was aligned to the frame mice began running (velocity crossed above 5cm/s).The average aligned uorescent activity of each ROI was then determined for each environment.

Linear regression analysis
To assess dynamics between each of the above measures and calcium activity of LC and VTA axons, we performed linear regression on the population's familiar environment data and signi cance was assessed with an F test.To compare the dynamics between LC and VTA axons, we performed exact testing based on Monte-Carlo resampling (1000 resamples with sample size matching the lower sample size condition) as detailed in legends (Fig. 1E).
To assess the changing position and velocity encoding of LC axons following exposure to a novel environment, we performed linear regression on the population uorescence data of the average of the last 4 laps in the familiar environment, and each of the rst three laps in the novel environment for each measure.The signi cance for the t of each line was assessed with an F test, and an ANCOVA was conducted to test for di erences in slope between the four laps.The same process was conducted for the motion initiation dynamics, but only using ROIs in mice who paused within the rst 2 laps and 30s following exposure to the novel environment.

Novel response analysis
To examine the response of LC and VTA axons to the novel VR environment, the uorescence data was normalized by the mean for each ROIs and aligned to the frame where the mice were switched to the novel environment and the mean normalized F for LC and VTA ROIs at each time point was calculated.Baseline uorescent activity was then calculated for LC and VTA ROIs separately by generating 1000 shu ed traces of the ROIs calcium activity and subsampling down to the sample size (90 for LC; 7 for VTA) 1000 times and nding the mean of the subsampled shu es.The mean and 95% CI of all 1000 subsamples was found and the mean activity of LC and VTA ROIs was considered signi cantly elevated when it passed above the 95% CI of the shu ed data.The same process was repeated to de ne a baseline for the time binned data ( uorescent activity divided into 50 frame bins) and the lap binned data (mean activity for each lap).
Additionally, to account for changes in behavior between the familiar and novel environments, periods where the animals were immobile (velocity f 0.2 cm/s) were removed and running periods were concatenated together and aligned to the switch to the novel environment.Here, we again de ned a baseline for the time mean traces, time binned activity, and the lap binned activity using the above bootstrapping approach.

Figure graphics
All gure graphics including Fig. 1A-B and Fig. 2 A were created using BioRender.com.iii.i.

VTA LC
Supplementary Figure 1.a,i, Population activity F _F ± s.e.m. binned by the virtual distance to reward for VTA ROIs (orange, 200m track n = 9 ROIs in 8 mice) and LC ROIs (blue, 300m track, n = 87 ROIs from 27 sessions in 17 mice) in the familiar environment.Linear regression, F test, VTA, P = 1.83e * 28, LC, P = 8.77e * 06.ii, The LC data set was resampled 1000x using n = 9 axons to match the number of VTA Rois and the slope and intercept of the regression line were measured each time (blue dots).The VTA slope is steeper than all LC slopes indicating a stronger positive relationship between position and activity for VTA inputs.iii,, Linear regression of position binned activity of individual VTA (orange diamonds), and LC (blue, circles) axons.The majority (8/9) of VTA axons show a signi cant positive relationship with position while LC axons show both a positive (21/87 axons from 9 sessions in 8 mice) and negative (32/87 axons from 15 sessions in 9 mice) relationship.b,i, Same data as (a, i,) averaged by time to reward.Linear regression shows that the population of VTA axons has a signi cant positive relationship with time to reward.Linear regression, F test, VTA, P = 4.44e * 25, LC,P = 0.119.ii, Resampling shows the VTA slope is above the resampled LC slopes indicating VTA ROIs have a stronger positive relationship with time to reward.iii, Linear regression of individual VTA and LC axons shows the majority (8/9) of VTA axons have a signi cant positive relationship with time to reward while LC axons show both a signi cant positive (31/87 axons from 14 sessions in 11 mice) and negative (17/87 axons from 7 sessions in 4 mice) relationship

Fig. 1 .
The same trends were seen for VTA and LC axons, with 8 VTA ROIs showing positive relationships with both distance and time from reward, and 21 LC ROIs showing a positive relationship with distance from reward and 31 LC ROIs showing a positive relationship with time to reward.This suggests that track length does not in uence the encoding properties of VTA and LC axons.

Figure 1 .
Figure 1.Distinct activity dynamics in VTA and LC axons during navigation of familiar environments a, Experimental setup (top), created with BioRender.com.Example virtual reality environment.b, Schematic representation of injection procedure (left).Representative coronal brain sections immunostained for Tyrosine Hydroxolase (TH) from a DAT-Cre mouse showing overlapping expression of axon-GCaMP (green) and TH (red) in VTA neurons (top) and from a NET-Cre mouse showing overlapping expression of axon-GCaMP(green and TH (red) in LC neurons (bottom).c,.Example CA1 eld of view of VTA axons (top) and LC axons (bottom).Extracted regions of interest used for example VTA and LC activity throughout the gure.d, Example DAT-Cre mouse (left) and NET-Cre mouse (right) with aligned reward delivery (top, green), mouse track position (black), F _F from example roi (VTA-orange, LC-blue), and mouse velocity (bottom, gray).

Figure 1
Figure1(continued).e, i, Position binned F _F ± s.e.m in example VTA (orange) and LC (blue) ROIs during navigation of the familiar rewarded environment.ii, Population average position binned F _F ± s.e.m. in VTA ROIs (orange, n = 9 ROIs from 8 mice) and LC ROIs (blue, n = 87 ROIs from 27 sessions in 17 mice) .Linear regression analysis (on all data points, not means) shows that the population of VTA ROIs increase activity during approach of the end of the track while the population of LC ROIs have consistent activity throughout all positions.Linear regression, F test, VTA, P < 1e * 21, LC, P < 0.01.iii, The LC data set was resampled 1000x using n = 9 ROIs to match the number of VTA ROIs and the slope and intercept of the regression line were measured each time (blue dots).The VTA slope is steeper than all LC slopes indicating a stronger positive relationship between position and activity for VTA axons.iv, Linear regression of position binned activity of individual VTA (orange diamonds), and LC (blue, circles) ROIs.The majority (8/9) of VTA ROIs show a signi cant positive relationship with position while LC ROIs show both a positive (25/87) and negative (37/87) relationship.F, i, Same example ROIs as (d) binned by velocity.ii, Same data as (d, ii,) binned by velocity.Linear regression shows that the population of VTA and LC ROIs have a signi cant relationship with velocity.Linear regression, F test, VTA, P < 0.05, LC,P < 1e * 68.iii, Resampling shows the VTA slope and intercept is within the resampled LC slopes and intercepts indicating similar relationships with velocity.iv, Linear regression of individual VTA and LC axons shows the majority (63/87) of LC ROIs have a signi cant positive relationship with velocity while only 2 VTA ROIs show this relationship.g, Same example ROIs as (d) aligned to motion onset.ii, Same data as (d, ii,) aligned to motion onset.Linear regression shows that the population of VTA axons have a negative slope prior to motion onset while LC axons have positive slope.Linear regression, F test, VTA, P < 0.01, LC, P < 1e * 65. iii, Resampling shows the VTA slope is negative while all resampled LC slopes are positive.iv, Linear regression of individual VTA and LC ROIs shows the majority (56/87) of LC ROIs have a signi cant positive slope prior to motion onset while the majority (6/9) of VTA ROIs have a negative slope.

147
to most periods of immobility occurring between reward delivery and the start of the next lap, 148 during which we previously demonstrated reward related activity decays in VTA axons (Krishnan 149 et al., 2022).Indeed, in the unrewarded condition the negative slope of the VTA population activity 150 leading up to motion onset disappears (Fig. 2B.iii.), indicating this relationship is largely driven by 151 the presence of reward rather than motion onset.However, the LC population activity remains 152 unchanged in the unrewarded environment (Fig 2C.iii.), suggesting this activity is related to the 153 lead up to motion initiation.These di erences in activity are not an artifact of lower sample size 154 of VTA ROIs as shown by down-sampling the LC ROIs activity 1000 times and measuring the slopes 155 and intercepts of the down-sampled data did not generate any data points that overlapped with 156 the VTA slope and intercept (Fig 1G.iii).In further support of distinct activity pro les leading up 157 to motion onset, we found that the majority (6/9 ROIs in 6 mice) of VTA ROIs decreased in activity 158 leading up to motion onset but only 2/87 LC ROIs in one session decreased in activity, while the 159 majority, (56/87 ROIs in 17 sessions in 9 mice), of LC ROIs increased in activity leading up to motion 160 onset (Fig. 1G.iv).Together, this analysis demonstrates overlapping but distinct activity in VTA and 161 LC axons during spatial navigation with VTA axons showing strong activity correlated with distance 162 to reward and some velocity correlated activity, while LC axons demonstrate activity correlated to 163 velocity and time to motion onset. 164

172
Environmental Novelty induces activity in LC but not VTA inputs 173Both LC and VTA neurons have been shown to respond to novel environments(Takeuchi et al.,   174   2016).Therefore, we aimed to examine the activity of VTA DA and LC inputs to the hippocampus 175

Figure 3 .
Figure 3. Exposure to a novel environment causes an abrupt and sustained increase in activity in LC but not VTA inputs to dCA1 a, i, Experimental Paradigm.ii, Behavior from example mouse during the transition from the familiar VR environment to a novel VR environment showing the animals track position (top, black) and velocity(bottom, gray).iii, The average running velocity ± s.e.m. of all mice during the transition to a novel VR environment (top).The average freezing ratio of all mice ± s.e.m., calculated as the time spent immobile (velocity < 5cm/s) divided by the total lap time.Each lap was compared to the nal lap in the familiar environment using a one-way ANOVA with Tukey HSD post hoc test (Blue *, NET-Cre mice P < 0.05; Orange *, DAT-Cre mice P < 0.05).b, Mean normalized uorescence of all VTA ROIs (top, n = 7) and LC ROIs (bottom, n = 87) aligned to the switch to the novel environment.To de ne a baseline and 95% CI (gray shaded region), 1000 shu es were created from the calcium traces and down sampled to match the sample size and averaged.This was repeated 1000 times and the mean and 95% CI of this shu ed data was determined for each frame.Red lines indicate periods where two or more consecutive frames passed above the % CI of the shu ed baseline.c, Normalized F _F activity of all VTA ROIs (top) and LC ROIs (bottom) aligned to the switch to the novel VR environment.d, The normalized uorescence of all LC ROIs binned by lap (left) or into 50 frame bins (right).The baseline and 95% CI (gray shaded region) was de ned using the same method as in (b) Red lines indicate bins above the baseline 95% CI.

Figure 4 .
Figure 4. Novelty-induced changes in behavior explain the late but not early increases in LC activity a, Good behavior from example mouse following removal of freezing periods (velocity < 0.2 cm/s) during the transition from the familiar to a novel VR environment showing the animals track position (top, black) and velocity (bottom, gray).b, Normalized F _F activity of all LC ROIs aligned to the switch to the novel VR environment following removal of freezing periods.c, Mean normalized F of all LC ROIs (bottom, n = 87) aligned to the switch to the novel environment.To de ne a baseline and 95% CI (gray shaded region), 1000 shu es were created from the calcium traces and down sampled to match the sample size and averaged.This was repeated 1000 times and the mean and 95% CI of this shu ed data was determined for each frame.Red lines indicate periods where two or more consecutive frames passed above the 95% CI of the shu ed baseline.d,e,.The normalized F of all LC ROIs binned by lap (d) or into 50 frame bins (e).The baseline and 95% CI (gray shaded region) was de ned using the same method as in (c).Red lines indicate bins above the baseline 95% CI. f-h, Population average position binned (f), velocity binned (g), and motion onset aligned (h) F _F ± s.e.m. in LC ROIs (n = 90) in the nal laps of the familiar environment (light blue), and the rst (orange), third (dark blue), and nal laps (green) of the novel environment.Linear regression, F test, position binned (f) fam last, P < 1e * 4, nov 1, P < 1e * 68, nov 3, P = 0.48, nov last, P < 0.001; velocity binned (g) fam last, P < 1e * 31, nov 1, P < 0.05, nov 3, P < 1e * 5, nov last, P < 1e * 13; motion onset aligned (h) fam last, P < 1e * 29, nov 1, P < 0.001, nov 3, P < 1e * 9, nov last, P < 1e * 18.The slope of each lap was compared to the nal familiar laps using a one-way ANCOVA with Tukey HSD post hoc test.* P < 0.01, ** P < 0.001, *** P < 1e * 4.