Elucidating a locus coeruleus-dentate gyrus dopamine pathway for operant reinforcement

  1. Elijah A Petter
  2. Isabella P Fallon
  3. Ryan N Hughes
  4. Glenn DR Watson
  5. Warren H Meck
  6. Francesco Paolo Ulloa Severino
  7. Henry H Yin  Is a corresponding author
  1. Department of Psychology and Neuroscience, Duke University, United States
  2. Department of Cell Biology, Duke University School of Medicine, United States
  3. Department of Neurobiology, Duke University School of Medicine, United States

Abstract

Animals can learn to repeat behaviors to earn desired rewards, a process commonly known as reinforcement learning. While previous work has implicated the ascending dopaminergic projections to the basal ganglia in reinforcement learning, little is known about the role of the hippocampus. Here, we report that a specific population of hippocampal neurons and their dopaminergic innervation contribute to operant self-stimulation. These neurons are located in the dentate gyrus, receive dopaminergic projections from the locus coeruleus, and express D1 dopamine receptors. Activation of D1 + dentate neurons is sufficient for self-stimulation: mice will press a lever to earn optogenetic activation of these neurons. A similar effect is also observed with selective activation of the locus coeruleus projections to the dentate gyrus, and blocked by D1 receptor antagonism. Calcium imaging of D1 + dentate neurons revealed significant activity at the time of action selection, but not during passive reward delivery. These results reveal the role of dopaminergic innervation of the dentate gyrus in supporting operant reinforcement.

Editor's evaluation

These important findings indicate that dopamine signaling, arising from the locus coeruleus, and D1R expressing neurons in the dentate gyrus can support positive reinforcement. This is an exciting finding given the prior dearth of information on the role of dopamine signaling in the dentate gyrus. The evidence to support the claims is compelling. Rigorous optogenetic experiments, site specific pharmacology, tracing, and calcium imaging bring together a compelling argument that dopamine signaling in the dentate can play an important role in positive reinforcement. This manuscript will be of interest to those interested in dopamine, locus coeruleus and/or hippocampal function, learning or motivated behaviors.

https://doi.org/10.7554/eLife.83600.sa0

Introduction

In operant learning, animals modify their action repertoires to earn desired rewards. Previous work on the neural substrates of such learning has focused on the striatum and the midbrain dopaminergic projections that target the striatum (Wise, 2004; Yin et al., 2005; Kravitz et al., 2013; Rossi et al., 2013; Yttri and Dudman, 2016). Midbrain dopamine neurons have been implicated in reinforcement learning (Schultz et al., 1997; Tsai et al., 2009; Rossi et al., 2013). Such learning is often thought to be distinct from declarative or episodic learning, which requires the hippocampus and medial temporal lobe structures (Mishkin et al., 1984; Morris et al., 1986; Milner et al., 1998; Eldridge et al., 2000). On the other hand, work in both humans and rodents has also implicated the hippocampus in reward processing and motivated behavior, though the underlying mechanisms remain unclear (Adcock et al., 2006; Gauthier and Tank, 2018).

The hippocampus is also a target of dopaminergic projections. Dopamine receptors are expressed in the hippocampus, and in mice D1-class receptor expression is common in the dentate gyrus (DG) region (Gangarossa et al., 2012; Kempadoo et al., 2016). However, these dopaminergic projections come from locus coeruleus (LC) (Kempadoo et al., 2016; Takeuchi et al., 2016; Chowdhury et al., 2022), rather than the major dopamine cell groups in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc), which supply dopamine to the basal ganglia (Björklund and Dunnett, 2007; Ikemoto, 2007). The functional role of the dopaminergic LC-DG projection remains obscure.

In this study, we examined the contribution of dopaminergic signaling in the DG to operant learning and behavior. We found that mice could learn to perform a new action (pressing a lever) for optogenetic activation of D1 + neurons in the DG. In addition, using both optogenetics and in vivo pharmacological manipulations, we found that activation of LC dopaminergic neurons that project to the DG can also support self-stimulation. This effect depended on the activation of D1-like receptors. Finally, using in vivo calcium imaging in appetitive operant conditioning with food rewards, we found that D1 + DG neurons were more related to the goal-directed actions than simply non-contingent reward presentation.

Results

To understand the role of D1 + neurons in the hippocampus, we tested whether selective stimulation of these neurons can reinforce operant behavior using a self-stimulation paradigm. We injected either a Cre-dependent channelrhodopsin (AAV5-DIO-ChR2) or a fluorescent control (DIO-eYFP) into D1-Cre mice (D1::ChR2DG or D1::eYFPDG), producing selective expression of the excitatory opsin in D1 + neurons in the dentate gyrus (Figure 1A–B). Mice received photo-stimulation (500 ms, 20 Hz, 15 ms pulse width) following lever pressing on a fixed ratio schedule of reinforcement (Figure 1C). All D1::ChR2DG mice learned to press a lever for stimulation, whereas control mice did not (Figure 1D). These results suggest that D1::ChR2DG stimulation is sufficient to reinforce lever pressing. Interestingly, this form of self-stimulation is remarkably resistant to extinction, persisting after 8 days without any photostimulation.

Optogenetic stimulation of D1 + neurons in the dentate gyrus is sufficient for operant self-stimulation.

(A) Schematic of optic fiber placement above virally infected D1 + dentate gyrus (DG) neurons in D1-Cre mice. (B) Left, coronal section showing ChR2 expression in the DG. Right, magnified view of AAV infected DG neurons from inset colocalized with D1 receptors. White arrows indicate cell bodies. (C) Schematic of the operant self-stimulation chamber. (D) Lever pressing rate across three fixed ratios (FR1, FR3, FR5) schedules of reinforcement, and extinction (8 days each) for D1:Chr2-DG animals (n=8) and eYFP (n=8) controls. D1::Chr2-DG mice self-stimulated significantly more than controls (Two-way RM ANOVA, Group [ChR2 or eYFP] × Day, main effect of group F(1,14) = 16.59, p=0.0011, main effect of Day, F(23, 322) = 2.958, p=0.0078, and a significant interaction between day × group F(23,322) = 2.479, p=0.0003). During extinction, there was a significant main effect of group: F(1,112) = 58.87, p<0.0001, no significant effect of Day: F(7, 112) = 0.4571, p=0.8635, and no interaction: F(7, 112) = 0.8243, p=0.8243. Means +/−SEM for all graphs. DG, dentate gyrus; LC, Locus Coeruleus; scp, superior cerebellar peduncle; DAPI, 4′,6-diamidino-2-phenylindole. ****p<0.0001.

Figure 1—source data 1

The press rate (presses/min) of D1::ChR2 and D1::eYFP mice across FR1, FR3, and FR5 sessions.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig1-data1-v2.csv
Figure 1—source data 2

The press rate (presses/min) of D1::ChR2 and D1::eYFP mice across extinction sessions.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig1-data2-v2.zip

Next, using retrograde tracing methods, we were able to map projections to the DG (Figure 2A–D & Figure 2—figure supplement 1). We confirmed significant LC projections to the DG, but we did not find significant VTA or SNc projections (Figure 2E and H & Table 1). Retrograde labeling of DG-projecting LC neurons is colocalized with tyrosine hydroxylase (TH), a marker for catecholamine neurons (e.g. dopamine, norepinephrine; Figure 2F–H). In contrast, there was no labeling in the VTA (Figure 2H, Figure 2—figure supplement 1). This finding suggests that the DG receives TH + projections from the LC rather than VTA.

Figure 2 with 1 supplement see all
Retro-Cre tracing shows that the main catecholamine input to the dentate gyrus (DG) is the locus coeruleus (LC), not the ventral tegmental area.

(A) Schematic of Retro2-Cre injection into the dentate gyrus in Ai-14 reporter mice. (B) Schematic summarizing all brain regions that project to the DG. Only LC is TH+. Abbreviations – Vertical diagonal band (VDB), Medial septal nucleus (MSN), Ventral lateral preoptic area (VLPO), retro mammillary bodies (RMM), dorsal lateral entorhinal cortex (DLEnt) (C) Injection site of the Retro2 showing the Cre-positive neurons. (D–G) Retrograde labeling of neurons in canonical brain regions that project to the hippocampus. (D) Entorhinal cortex (EC). (E) Limited retrograde labeling of neurons in the VTA, colocalized with tyrosine hydroxylase (TH). (F & G) Retrograde labeling of LC neurons in two out of four mice, colocalized with tyrosine hydroxylase. (H) Percent of colocalized neurons in the LC (n=8; four mice × two hemispheres) and VTA (n=6; three mice × two hemispheres). Unpaired t-test, p<0.0001. Mean and +/−SEM.

Figure 2—source data 1

Percent of colocalized (TH+ and tdTomato+) neurons in the DG and VTA.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig2-data1-v2.csv
Table 1
Colocalization of tyrosine hydroxylase with retrograde dentate gyrus (DG) labeling in the locus coeruleus (LC).

Colocalization of tyrosine hydroxylase (TH) and Retro-cre labeling (n=8; four mice, two hemispheres), showing that at least some of the LC-DG neurons are TH positive. In contrast, no colocalization was found with retro-Cre and TH labeling in the VTA (n=6; three mice, two hemispheres), and ventral tegmental area (VTA) slices were not taken from one animal.

AnimalRetro-cre label VTA (from DG)Retro-Cre label LC (from DG)LC colocalized with TH
Mouse 1 (LH)0184
Mouse 1 (RH)0327
Mouse 2 (LH)052
Mouse 2 (RH)0136
Mouse 3 (LH)02012
Mouse 3 (RH)03115
Mouse 4 (LH)n/a2212
Mouse 4 (RH)n/a145

We then tested whether the LC-DG projection is responsible for the self-stimulation effect observed. In order to manipulate the LC-DG pathway selectively, we injected AAV-Retro2-Cre into the DG and a Cre-dependent ChR2 (AAV5-DIO-ChR2) into the LC (Figure 3A–B). We found that ChR2DG-LC (n=8) mice also showed self-stimulation that is comparable to the stimulation of D1::ChR2DG neurons (Figure 3C).

Locus coeruleus (LC) projections to the dentate gyrus (DG) contribute to operant learning.

(A) Schematic showing selective targeting of LC-DG projection. (B) Left: Representative coronal section showing Cre-dependent ChR2 expression in the LC. Right: Magnified view from inset showing eYFP colocalization with tyrosine hydroxylase (TH) LC neurons. (C) Lever presses per session for three fixed ratios (FR1, FR3, FR5) schedules of reinforcement, and extinction (no stimulation following lever pressing) for Retro::ChR2 DG-LC animals (n=8) and eYFP (n=8) controls. Mice with ChR2 expressed in LC self-stimulated significantly more than controls (Two-way RM ANOVA Group [ChR2 or eYFP] × Day, the significant effect of group: F(1,14) = 13.09, p=0.0031), the significant effect of day, F(23, 322) = 3.601, p<0.0001; interaction of day × group: F(23,322) = 2.712, p<0.0001. (D) Left, design of pharmacological experiments. After FR1 training, mice received IP injections of antagonists for either D1 (SCH 23390), or NE-beta receptors (propranolol). (E) D1-antagonist SCH 23390 significantly reduced self-stimulation of DG-projecting LC neurons: F(2, 14) = 6.9, p=0.008. Post hoc analysis (Dunnett’s) shows that both doses reduced lever pressing relative to controls (0.1 mg/kg, p=0.02; 0.2 mg/kg, p=0.007). (F) The NE antagonist propranolol did not have any effect: F(2, 14) = 0.290, p=0.753. Means +/−SEM. HPC, hippocampus; 4 v, fourth ventricle. *p<0.05, **p<0.01.

Figure 3—source data 1

The press rate (presses/min) of LC-DG::ChR2 LC-DG::eYFP mice during extinction days.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig3-data1-v2.csv
Figure 3—source data 2

The press rate (presses/min) of LC-DG::ChR2 LC-DG::eYFP mice across FR1, FR3, and FR5 sessions.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig3-data2-v2.csv
Figure 3—source data 3

Total presses during vehicle and propranolol I.P. administration (3mg/kg and 6mg/kg).

https://cdn.elifesciences.org/articles/83600/elife-83600-fig3-data3-v2.zip
Figure 3—source data 4

Total presses during vehicle and SCH-23390 I.P. administration (0.1mg/kg and 0.2mg/kg).

https://cdn.elifesciences.org/articles/83600/elife-83600-fig3-data4-v2.zip

The LC-DG projection releases both norepinephrine and dopamine (Kempadoo et al., 2016; Takeuchi et al., 2016). It is unclear which transmitter is responsible for the self-stimulation effect, though our observation on D1 + DG neurons (Figure 1) suggests that dopamine might be responsible. Consequently, to determine which of these transmitters is responsible for the observed effects, we used pharmacological manipulations in combination with pathway-specific optogenetic manipulations using the same self-stimulation paradigm (Figure 3E and Figure 4). Mice (n=8) trained on self-stimulation were tested after either receiving systemic injections of a β-adrenoceptor antagonist (propranolol), or a D1-antagonist (SCH 23390). β-adrenoceptor blockade did not produce any significant effects (Figure 3D and F). In contrast, D1-antagonist significantly impaired self-stimulation (Figure 3E).

Local infusions of D1 but not NE beta antagonist into the DG reduces self-stimulation.

(A) Schematic showing injection strategy for local drug infusions into the DG during self-stimulation of LC neurons that project to the DG (n=8). Cre expression was induced in LC neurons projecting to the DG by first injecting AAV-Retro-2 into the DG. An injection of a Cre-dependent virus (AAV5-DIO-ChR2-eYFP) was then made in the LC before optic fiber implantation. Canulae were used to inject DA and NE antagonists into the DG. (B) Top, representative coronal section showing ChR2 expression in the LC. Bottom, ChR2 terminals in DG and cannula tracks. (C) Acquisition of lever pressing Retro::ChR2 DG-LC mice or Retro::eYFP DG-LC (controls). Experimental animals self-stimulated more than controls (Two-way ANOVA [Day × Group], effect of Day F(7, 84) = 5.222, p<0.0001; effect of group, F(1,12) = 37.98, p<0.0001; interaction F(7,84) = 4.932, p<0.0001). (D) One-way RM ANOVA showed D1 antagonist SCH-23390 significantly reduced lever pressing. There is a significant drug effect: F(2,14) = 39.16, p<0.0001. Dunnett’s multiple comparisons show both doses produced significant suppression of lever pressing (1.8 mM, p<0.0001; 3.6 mM, p<0.0001). (E) NE antagonist propranolol had no significant effect on self-stimulation. F(2,14) = 0.1912, p=0.8281. Dunnett’s multiple comparisons show no significant differences 10.5 mM, p=0.7829; 21 nM, p=0.8624. (F) Using DeepLabCut we tracked the distance traveled by the each animal and found no significant differences in the movement for the vehicle, SCH23390 (3.6 mM) or propranolol (21 nM) RM one-way ANOVA no effect of group, F(1, 4) = 3, p=0.1516. Means +/−SEM for all graphs. DG, dentate gyrus; LC, locus coeruleus; 4 v, fourth ventricle.

Figure 4—source data 1

Total presses during vehicle and propranolol administration through cranial cannula (21mM and 10.5mM).

https://cdn.elifesciences.org/articles/83600/elife-83600-fig4-data1-v2.zip
Figure 4—source data 2

Total presses during vehicle and SCH-23390 administration through cranial cannula (3.6mM and 1.8mM).

https://cdn.elifesciences.org/articles/83600/elife-83600-fig4-data2-v2.zip
Figure 4—source data 3

The press rate (presses/min) of LC-DG::ChR2 + DG cannula and LC-DG::eYFP + DG cannula mice across FR1 sessions.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig4-data3-v2.csv
Figure 4—source data 4

Total head movement (meters) during SCH-23390(3.6mM) and propranolol(21mM) administration through cranial cannula.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig4-data4-v2.zip

To activate DG-projecting LC neurons robustly, we targeted the cell bodies of the DG-LC projections. But the LC has broad projections to many brain areas (Schwarz and Luo, 2015). To verify that our self-stimulation effects were not due to the activation of LC collaterals in other regions, we performed local infusions of antagonists (Figure 4A–C, n=8). Infusions of a D1-antagonist into the DG significantly impaired self-stimulation, whereas propranolol showed no significant group differences in self-stimulation (Figure 4D–E). These results suggest that the reinforcing effects of LC-DG stimulation are due to the activation of D1 receptors by dopamine, rather than by norepinephrine.

Based on our self-stimulation results, we hypothesized that DG D1 + neurons may be preferentially activated during operant conditioning in general, including during actions that result in natural rewards rather than simply optogenetic stimulation of the LC-DG pathway. To test this, we performed in vivo calcium imaging of DG D1 + neurons while performing an operant lever pressing task with a food reward. We implanted a gradient index lens above the DG in D1-Cre mice (n=5) and injected them with a Cre-dependent calcium indicator (AAV9-syn-FLEX-jGCamp7f) (Figure 5A–B).

D1 + dentate gyrus neurons are significantly modulated by lever pressing during operant conditioning.

(A) A schematic of a UCLA miniscope and GRIN lens implanted over jGCamp7f-infected D1 + cells in the dentate gyrus (DG) of D1-Cre mice for in vivo imaging (n=5). (B) Left: Representative coronal section showing GCaMP7f expression in D1 neurons of the DG. GRIN lens, marked in white. Middle: Imaging field of view with contours of identified neurons. Right: For example, calcium traces are significantly modulated by lever presses. (C) An example neuron showing increased calcium transient during lever pressing. Top: Heat map shows normalized calcium activity aligned to a single press as a function of time during fixed-ratio (FR) 1 trial. Bottom: Averaged calcium activity across all presses. (D) The press rates for FR1, FR3, and FR5 for each day of testing. The press rates increase across the FR schedule. (E) The percent of neurons modulated by the first press in each FR schedule across days. Two-way RM ANOVA FR schedule × Day, no effect of FR schedule F(2,4) = 0.6702, p=0.5298; effect of day, F(2,4) = 4.536, p=0.0213; no effect of interaction F(2,4) = 0.6403, p=0.6389 (F) The percent of neurons modulated by a non-contingent reward task, or by each press in an FR5 task. Day 1, One-way RM ANOVA F(4,5) = 3.389, p=0.0222. Day 2, One-way RM ANOVA F(4,5) = 16.19, p=0.0046. Day 3, One-way RM ANOVA F(4,5) = 4.807, p=0.0048. Dunnett’s multiple comparison tests was used to compare the percent of press-modulated neurons to the non-contingent modulated neurons. Significance values are marked (*) for p<0.05.

Figure 5—source data 1

Lever press rate (presses/min) during FR1, FR3 and FR5 sessions, number of modulated neurons during the first press, percent of modulated neurons during each press across days.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig5-data1-v2.xls

We then recorded calcium transients from DG D1 + neurons during operant lever pressing for food rewards (Figure 5A–C) during lever pressing for food reward on fixed-ratio (FR) schedules (FR1, FR3, and FR5). We found distinct populations of DG D1 + neurons that were modulated by lever pressing. To see if the neural activity is action-contingent, we also used a control task in which pressing is not required. The reward was delivered non-contingently every 20 s, preceded by 1 s of white noise. On this task, there were far fewer significantly modulated DG D1 + neurons (n=6, 3% of the total population) compared to the operant task (Figure 5F, Table 2). To verify that the virus targets D1 + neurons in the DG, we quantified the percentage of neurons that are virally targeted that express D1 receptors. Using RNA scope, we found that GcAMP-7f was colocalized with D1 receptors (Figure 6).

Colocalization of GcAMP-7f with D1 RNA scope probes.

(A) Left, the representative coronal section from a Drd1a-cre mouse injected with GCaMP-7f. Right, zoomed-in view of green (GCaMP7f) and red (probe) channels, showing the outlines (white) of GFP + cells identified in FIJI. (B) Left, the percent of GFP cells colocalized with the negative control probe (n=3 sections; one brain, nine images), positive control probe (n=3 sections; one brain, five images), and Drd1a probe (n=7 sections; three brains, 38 images). One-way ANOVA showed a significant group difference (F = 229.2, p<0.0001). About 80% of GFP neurons colocalized with the Drd1a, much higher than the background negative control probe (Tukey‘s, p<0.0001). Right, there was also a significant group difference in puncta per cell (ANOVA, F = 19.51, p=0.0004) between Drd1a probe and negative control probe (Tukey‘s, mean diff = –5.619, adjusted p<0.0052) and positive control probe (Tukey‘s, mean diff = 4.381, adjusted p=0.0226). Thus the SIO/DIO constructs are only being expressed in D1 + neurons. Scale bars represent 10 um.

Figure 6—source data 1

Percent of GFP cells colocalized with negative control, positive control and Drd1a probes.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig6-data1-v2.xls
Table 2
Calcium imaging neuron counts for each task.
TaskAnimal 1Animal 2Animal 3Animal 4Animal 5
Delay Task64622259100
FR1 Day 14053318285
FR1 Day 24248269945
FR1 Day 32648209446
FR3 Day 12654239032
FR3 Day 22771237533
FR3 Day 32775278033
FR5 Day 13291259843
FR5 Day 2281062710057
FR5 Day 333106287557
FR5 switch Day 19965185663
FR5 switch Day 210064204963
FR5 switch Day 39666215462
Non-contingent12394237896

To determine if the activity of these neurons reflected the spatial locations of the lever pressing or the action of the lever pressing itself, we used a discrete trial design with two levers (Figure 7). On each trial, one of the two levers was randomly selected to extend into the operant box. Once pressed, the lever would retract. The reward would then be delivered 1 s later. This task allowed us to compare the neural activity modulated by lever pressing and reward, as well as determine the spatial tuning of the same neurons. We found that several populations of dentate D1 + neurons (n=40, 16.5% of the total population) that were significantly modulated at the time of lever pressing (Figure 7C). One small population was modulated by reward delivery (n=14, 4.9% of the total population). Importantly, another population with significantly more neurons was responsive to lever pressing at either lever location (Figure 7C–D; n=22, 9.79% of the total population). These neurons were not spatially selective, as they were responsive when the lever was presented at different locations. However, we did find a small population that responded to only a single lever (Figure 7C; left lever: n=15, right lever: n=12; 4.2% of total population).

Figure 7 with 2 supplements see all
D1 + neurons in dentate gyrus (DG) are significantly modulated by lever pressing, but not by passive reward delivery.

(A) One example neuron from each of the five calcium imaging animals, showed increased calcium transient during lever pressing. Top: Heat map shows normalized calcium activity aligned to a single press as a function of time during fixed-ratio (FR) 1 trial. Bottom: Averaged calcium activity across all presses. For the five different animals, we recorded n=64 (Animal 1), n=62 (Animal 2), n=22 (Animal 3), n=59 (Animal 4), and n=100 (Animal 5) (B) Top: Schematic of FR1 paradigm. Animals press one of two levers that are randomly presented, which then retracts followed by pellet delivery 1 s later. After 2 s, one of the two levers extends again at random. Bottom: Peri-event heat maps and average traces of calcium activity aligned to either all lever presses, reward, or lever extension. Only neurons that are significantly modulated around each event are shown. (C) Percentages of modulated neurons by each event. More neurons are modulated by lever pressing than reward delivery (One-way ANOVA, F(5,24) = 4.1077, p=0.0078; Dunnett’s multiple comparisons: Lever press vs reward p<0.05). (D) Venn diagram displaying the number of neurons in a session modulated by spatial location (place), lever pressing, or reward delivery.

Figure 7—source data 1

Percent of modulated neurons during lever press, both levers, left lever only, right lever only, reward, and extension.

https://cdn.elifesciences.org/articles/83600/elife-83600-fig7-data1-v2.zip

It is difficult to assess the spatial activity of neurons in operant tasks, as animals do not cover the arena equally but instead preferentially occupy specific task-relevant locations (Figure 7—figure supplement 1). To examine the stability of spatially related activity during operant conditioning, we used an FR5 task with two levers (Figure 7—figure supplement 2). We then used the same methods as described in Skaggs et al., 1993 to identify spatially modulated neurons, and split the sessions into periods when the left or right lever was active. This allowed us to recalculate the center of mass of our identified spatial firing fields with two different lever locations. As mice mostly stayed close to the wall where the two levers and food port were located, we limited our analysis to the x-dimension which explained most of the variance in the neural activity. We found that the place field centers were significantly different when the lever is available. While we did find that occupancy varied in this switch task, the occupancy across other tasks was consistent, suggesting that spatial modulation does not depend on the type of task (e.g. operant vs Pavlovian) (Figure 7—figure supplement 1). In contrast, task-related neural activity in the DG depends on whether the task is action-contingent.

Discussion

Together our results provide the first evidence that DG D1 + neurons may play a role in operant reinforcement. Using a cell-type-specific approach, we showed that activation of D1 + neurons in the DG is sufficient for self-stimulation (Gangarossa et al., 2012). Furthermore, our retrograde tracing identified that the DG receives TH + input from the LC and not from the VTA or SNc, suggesting that the LC is a source of dopaminergic projections to the DG. We found that mice will press a lever for optogenetic stimulation of the LC-DG projection. Blockade of D1 receptors, but not noradrenergic beta receptors, attenuated the self-stimulation of hippocampal-projecting LC neurons.

These findings build upon previous research that suggests that the LC supplies the primary dopaminergic input to the dorsal hippocampus (Kempadoo et al., 2016; Takeuchi et al., 2016). While previous work focuses on the LC-CA1 pathway, we examine a population of D1 + neurons in the DG that plays a role in operant reinforcement.

Both the hippocampus and the LC are known to be effective sites for intracranial self-stimulation (Ursin et al., 1966; Crow et al., 1972; Ritter and Stein, 1973). However, as non-selective electrical stimulation was used in classic studies, the precise circuit mechanisms underlying these observations remain unclear. In the present study, we have investigated a pathway for operant reinforcement that originates from LC neurons and targets D1 + DG neurons in the hippocampus. Future work will have to address whether this self-stimulation effect requires D1 + neurons or if it is a property of hippocampal neurons in general.

Self-stimulation behavior supported by stimulation of the LC-DG projections or the D1 + DG neurons appears to be different from that supported by stimulation of dopamine neurons in the VTA or SNc. First, the rate of lever pressing is much lower with LC-DG self-stimulation. Yet once established, the lever pressing was surprisingly resistant to extinction, persisting for many days after the termination of stimulation. This is very different from the self-stimulation of VTA or SNc DA pathways and their target regions; such classic self-stimulation behavior is rapidly extinguished in the absence of stimulation (Gallistel, 1964; Olds, 1977). Thus, classic self-stimulation supported by SNc and VTA DA neurons, which project mainly to the frontal cortex and basal ganglia, has a much stronger immediate effect on performance but rarely produces persistent actions in the long-term without stimulation. The effect on performance can largely be explained by a repetition of the action commands recently generated, but without DA release in the BG this repetition effect decays. Self-stimulation of the LC-DG pathway, in contrast, is less robust but is supported by some long-lasting memory of the stimulation, in accordance with recent findings that the LC projections to the hippocampus play a major role in long-term contextual memory (Chowdhury et al., 2022).

Our calcium imaging results showed that D1 + neurons in the DG are preferentially active during goal-directed instrumental actions compared to passive reward delivery. These findings suggest that the DA projections to the hippocampus contribute to the reinforcement of specific instrumental actions. They are broadly in agreement with recent findings on entorhinal grid cells (Butler et al., 2019) and hippocampal CA1 cells (Gauthier and Tank, 2018), as well as LC terminals in CA1 that were preferentially active near a novel reward location (Kaufman et al., 2020). It remains to be determined how the activity of DG D1 + neurons changes during activation of LC neurons that project to the hippocampus, and if LC activation induces plasticity in the DG that is important for learning.

Materials and methods

All experimental procedures were conducted in accordance with standard ethical guidelines and were approved by the Duke University Institutional Animal Care and Use Committee.

Subjects

All behavioral data were collected from D1-cre mice (Cre targeted to Drd1 locus, B6;129-Tg(Drd1-cre)120Mxu/Mmjax, Jackson Labs), and wild type (C57BL/6 J). Optogenetic control of D1-receptor expressing dentate hippocampal neurons was achieved with a double-floxed inverted recombinant AAV5 virus injection to express the excitatory opsin ChR2-eYFP. Viral infection in the dentate of the hippocampus was histologically verified with eYFP imaging colocalized against a D1 receptor antibody and DAPI staining. All mice were aged between 2–12 months old, and housed on a 12:12 light cycle, with tests occurring in the light phase. For calcium imaging experiments, mice were put on food restriction and maintained at 90% of their initial body weights.

Viral constructs

Request a detailed protocol

CAV2-Cre was obtained from Institut de Génétique Moléculaire de Montpellier. rAAV5.EF1α.DIO.hChR2(H134R).eYFP, rAAV5.EF1α.DIO.eYFP, AAV9.hSyn.FLEX.jGCaMP7F, AAV(retro2).hSyn.EF1α.Cre.WPRE was obtained from the Duke University Vector Core. pAAV_hSyn1-SIO-stGtACR2-FusionRed was from Ofer Yizhar (Addgene viral prep # 105677-AAV1; https://n2t.net/addgene:105677; RRID:Addgene_105677).

Pathway-specific retrograde tracing experiments

Request a detailed protocol

Retrograde anatomical tracing data was collected from Ai14 reporter mice (129S6-Gt(ROSA)26Sortm14(CAG-tdTomato)Hze/J, Jackson labs). Ai14 reporter mice have a loxP-flanked STOP cassette that is excised in the presence of Cre to promote transcription of a CAG promoter-driven red fluorescent protein variant (tdTomato). 50 nL of either Retro2-Cre (Figure 2), or canine adenovirus type 2 expressing Cre recombinase (CAV2-cre, Figure 2—figure supplement 1) was injected into the DG of Ai-14 reporter mice (AP: –2.0 mm relative to bregma, ML: ± 1.3 mm relative to bregma, DV: 2.0 mm from skull surface) (Soudais et al., 2001; Tervo et al., 2016).

RNAscope

Request a detailed protocol

Three D1-Cre male mice were injected with the AAV9-hSyn-Flex-GCaMP7f in the DG (–2.0 & –2.2 AP; +\− 1.3 ML; 1.8 DV. from bregma). 3–4 weeks after injections, mice were euthanized with CO2, and their brain was quickly harvested and frozen in OCT for future RNAscope experiments using the Advanced Cell Diagnostics kit and probes (ACD). Brains were sectioned using a cryostat at a thickness of 20 micrometers and directly mounted on superfrost slides; the slides were then stored at –80.

On the experiment day, 4% PFA in PBS was chilled at 4 °C in a PFA-safe IHC container. Slides were removed from the –80 and immediately immersed in the pre-chilled PFA for 15 min at 4 °C. After washing the slides with PBS, sections were dehydrated through a 5 min immersion at 50%, 70%, and 100% ethanol. Slides were then air-dried for 5 min at room temperature before incubating them for 30 min with protease IV (ACD #322336), then washed twice in PBS. Sections were then incubated with either the Drd1a probe (ACD #406491), the negative control probe (ACD #320871), or the positive control probe (ACD #320881) for 2 hr at 40 °C. Next, slides were washed twice using the wash buffer (ACD #310091) and then incubated for 30 min with Amp1, 15 min with Amp2, 30 min with Amp3, and finally 15 min with Amp4 Alt B-FL (ACD #320851). After the two washes, sections were then incubated with 5% Neutral Goat Serum (NGS) in 0.2% TBST for 1 hr at RT in the dark, then incubated for 1 hr with a primary antibody against GFP (1:1000; Millipore, AB16901) and for 2 hr with a secondary Alexa-fluorophore (488) conjugated antibodies (Invitrogen). Slides were mounted in Vectashield with DAPI (Vector Laboratories, CA) and a minimum of 5 images per mouse were acquired on an Olympus Fluoview confocal microscopy using a 60 X oil immersion objective.

Images were then processed using FIJI (https://imagej.net/Fiji/Download), and GFP + cells were identified and saved as individual ROIs. Using the GFP + cells, a mask was created to identify the presence of puncta (probe positive signals) within each ROI using the puncta analyzer plugin. The percentage of GFP + cells having puncta and the average number of puncta per GFP + cell were calculated in all three conditions. (Figure 6).

Histology and immunohistochemistry

Request a detailed protocol

Mice were anesthetized and transcardially perfused with 0.1 M phosphate-buffered saline (PBS) followed by 4% paraformaldehyde (PFA) in order to confirm viral expression as well as optic fiber and GRIN lens placement. To confirm placement, brains were stored in 4% PFA with 30% sucrose for 72 hrs. Tissue was then post-fixed for 24 hr in 30% sucrose before cryostat sectioning (Leica CM1850) at 60 µm. Fiber and lens implantation sites were then verified.

To confirm eYFP expression in LC and DG neurons, sections were rinsed in 0.1 M PBS for 20 min before being placed in a PBS-based blocking solution. The solution contained 5% goat serum and 0.1% Triton X-100 and was allowed to sit at room temperature for 1 hr. Sections were then incubated with a primary antibody (polyclonal rabbit anti-TH 1:500 dilution, Thermo Fisher, catalog no. P21962; polyclonal chicken anti-EGFP, 1:500 dilution, Abcam, catalog no. ab13970) in blocking solution overnight at 4 °C. Sections were then rinsed in PBS for 20 min before being placed in a blocking solution with the secondary antibody used to visualize TH neurons in the LC (goat anti-rabbit Alexa Fluor 594, 1:1000 dilution, Abcam, catalog no. ab150080; goat anti-chicken Alexa Fluor 488, 1:1000 dilution, Life Technologies, catalog no. A11039) for 1 hr at room temperature. Sections were mounted and immediately coverslipped with Fluoromount G with DAPI medium (Electron Microscopy Sciences; catalog no. 17984–24). The placement was validated using an Axio Imager.V16 upright microscope (Zeiss) and fluorescent images were acquired and stitched using a Z780 inverted microscope (Zeiss).

Co-localization analysis with tracing

Request a detailed protocol

In order to characterize projections to the dentate gyrus we injected 50 nL of AAV(retro2).hSyn.EF1α.Cre.WPRE into each hemisphere of the DG of Ai14 mice (four mice × two hemispheres, n=8; two females and two males). We then processed the slices and acquired images as described above. We opened the raw images taken from the Axio Imager V16 upright microscope (Zeiss) in Fiji to quantify the number of cells from a single coronal brain slice using eight-bit confocal images. A threshold was set to identify the neuronal cell bodies. The function ‘fill holes’ was then used to remove possible empty spaces within the selected cells. After converting the image to a mask, we ran the ‘Analyze Particle’ plug-in in Fiji to count the cells in each image. Using the Analyze Particle function, the masks taken were then counted to determine the number of co-localizing cells using the ‘Colocalization Threshold’ plug-in in Fiji.

Optogenetic experiments

Request a detailed protocol

Mice were anesthetized with 2.0 to 2.5% isoflurane mixed with 1.0 L/min of oxygen for surgical procedures and placed into a stereotactic frame (David Kopf Instruments, Tujunga, CA). Meloxicam (2 mg/kg) and bupivacaine (0.20 mL) were administered prior to the incision. To optogenetically stimulate D1 + neurons in the hippocampus, adult D1-cre mice were randomly assigned to D1::ChR2(DG) (n=8, five males, three females, 8–10 weeks old) or D1::eYFP(DG) control groups (n=8, four males, four females, 8–10 weeks old). Craniotomies were made bilaterally above the hippocampus and AAV5-DIO-ChR2 was microinjected into the dentate gyrus through a pulled glass pipette (200 nL each hemisphere at 1 nL/s, AP: 2.0 mm relative to bregma, ML: ± 1.3 mm relative to bregma, DV: 2.0 mm from skull surface) using a microinjector (Nanoject 3000, Drummond Scientific). Optic fibers (SFLC230-10; 200 um core, 0.35 aperture, Ø1.25 mm, 6.4 mm Long SS Ferrule for MM Fiber, Ø231 µm Bore Size) were then implanted bilaterally above the dentate gyri. For pathway-specific experiments, wild-type mice were used to selectively target LC-Hipp (n=8, four males, four females, 8–10 weeks old) neurons by bilaterally injecting AAV(retro2).hSyn.EF1α.Cre.WPRE into the dentate gyrus (150 nL each hemisphere) combined with a Cre-dependent ChR2 virus injection into the locus coeruleus (AP: –5.45 mm relative to bregma, ML: ± 1.10 mm relative to bregma, DV: 3.65 mm from skull surface) before optic fiber placement (AP: –5.45 mm relative to bregma, ML: ± 1.10 mm relative to bregma, DV: 3.50 mm from skull surface). Controls received eYFP injections and fiber implants (n=8, four males, four females).

In addition, eight mice (four males, four females) were used in local infusion experiments in the DG. LC and DG surgeries were the same as pathway-specific manipulations, with the addition of cannulas (P1 technologies. AP: –2.00 mm relative to bregma, ML: ± 1.8 mm relative to bregma, DV: –1.4 mm, at a 10-degree angle). All optic fibers were secured in place with dental acrylic adhered to skull screws. Mice were group housed and allowed to recover for one week before experimentation.

Operant self-stimulation

Request a detailed protocol

Standard operant boxes (Model ENV-007, MED Associates, Inc, Albans, VT) were housed in a light and sound-attenuating cubicles (Model ENV-019, MED Associates, Albans, VT). Each box is equipped with two levers. A Windows XP-based computer system running MED-PC Version IV Research Control & Data Acquisition System software (Med Associates, St. Albans, VT) is used to control the experimental equipment and record the data.

For self-stimulation experiments, a single lever was inserted at the start of the session. For each lever press animals received 500 ms of stimulation at 20 Hz (15 ms pulse width, 5 mW power). Animals were trained in 30 min sessions. Animals were tested for 32 consecutive days, and received eight sessions of FR1, FR3, and FR5, and then eight extinction sessions. To test for extinction, the lever was inserted but no stimulation was delivered for lever presses.

Drug injections

Request a detailed protocol

Even low doses of DA antagonists are known to reduce self-stimulation, but low doses of NE antagonists have no effect on self-stimulation behavior (Rolls et al., 1974). In the present study, we selected doses that minimized effects on movement or arousal. The same mice used above were retrained after extinction (three FR1 sessions) to press a lever for stimulation of LC neurons that projected to the dentate gyrus. They were then tested with DA or NE antagonists (n=8; three males, five females). Mice alternated between testing days where they received an intraperitoneal injections (SCH23390 (Tocris) at 0.1 mg/kg and 0.2 mg/kg), propranolol (Sigma Aldrich) at 3 mg/kg or 6 mg/kg, or vehicle (phosphate-buffered saline), and training days with no injections. Each mouse had one testing day for each dose and drug combination (five total), and the order of the injections was determined pseudo-randomly. Injections were given 30 min prior to the start of the session.

For local drug infusions with chronically implanted cannulae, we trained naive animals (n=5) for 8 days on self-stimulation and used the same experimental testing protocol for infusions of SCH23390 (1.8 mM or 3.6 m) or propranolol (10.5 mM 21 mM). The dose is determined based on previous work (Takeuchi et al., 2016). The drugs were infused at a rate of 0.0005 mL.

Calcium imaging experiments

Request a detailed protocol

In order to target the dentate gyrus, AAV9-syn-FLEX-jGCamp7f was injected (50 nL) in four penetrations (A.P –2.0, M.L. 1.3, D.V. −2.2, –2.1, −2.0, –1.9) of D1-Cre mice (n=5, five males, 8–10 weeks old). A gradient index (GRIN) lens (Inscopix: 1 mm × 4 mm, 1.8 mm DV) was then implanted over the injection site. Viral expression was checked three weeks post-injection, and a base plate was secured to the skull with dental cement. A UCLA miniscope was used to assess in vivo activity of D1 + neurons in the DG in freely moving animals. Images were collected from the miniscope using Bonsai (Lopes et al., 2015). This allowed for the simultaneous collection of calcium data with behavioral videos (Logitech c920). Calcium traces were then motion corrected (https://github.com/flatironinstitute/CaImAn; Flatiron Institute, 2023) and extracted using constrained nonnegative matrix factorization for calcium imaging data (https://github.com/zhoupc/CNMF_E; Zhou, 2020; Zhou et al., 2018). The extracted traces were then analyzed using custom MATLAB scripts. Imaging sessions lasted 8–10 min.

FR training with a food reward (calcium imaging)

Request a detailed protocol

The behavioral tests used for calcium imaging were performed while the mice were food deprived to 85% of their free-feeding weight. There were six tasks used, with three days of testing for each task, for a total of 18 testing days. Each imaging session lasted approximately 10 min. Five mice were trained on a FR1 task where they received a pellet for pressing a lever. Subsequently, animals were moved to an FR3, and then FR5.

To examine the interaction of spatial location and lever pressing we used an FR5 switch task (Figure 7—figure supplement 2). In this task, a single lever into an operant chamber, and five responses resulted in a pellet. Following five pellets (25 presses), the first lever is retracted, and a second lever is inserted. The mouse has to move to the other side of the food cup and press the other lever to earn a food reward.

In order to dissociate reward delivery and action production we used an FR1 schedule of reinforcement where the reward was delayed one second after the press (Two-lever FR1 delay task). We used two levers in this task. One lever is inserted on a given trial. If pressed the lever would retract, and a food pellet is delivered 1 s later. After a 2 s inter-trial interval, one of the two levers was randomly inserted. Following FR testing, mice received a reward at fixed intervals (20 s), preceded by 1 s of white noise (Non-contingent reward). The animals were not required to press a lever to receive the reward.

Analysis

To assess significant increases in calcium activity, for each neuron we made a peri-event time histogram (PETH) between –3 s and 3 s aligned to relevant events (da Silva et al., 2018). Event times were considered between –500 ms and +500 ms around the relevant event. Baseline activity was considered –3000 to –1000 ms prior to the event. If the calcium activity was 99% above baseline for three consecutive 100ms bins then there was considered to be a significant increase in calcium activity. For tasks that involved two levers, we calculated the percent of neurons that were significantly modulated by either lever, and then excluded these neurons from our analysis of modulation by a specific lever. We identified spatially tuned cells by computing the spatial information contained in the calcium transients, compared with shuffled data (Skaggs et al., 1993). The data arena was split into 15 by 15 bins (2 × 2 cm each), and neurons were required to be active in active in at least three bins to be considered spatially modulated.

Movement tracking (DeepLabCut)

Request a detailed protocol

To ensure the different compounds were not affecting gross movement we tracked the distance traveled during the vehicle, SCH23390, and propranolol infusions. To do this we used DeepLabCut (Mathis et al., 2018). For each video, 40 frames were labeled and the model was trained for 100,000 iterations. Outlier frames were extracted and relabeled and then the model was retrained. To ensure we had equal data sets 20 min of data was selected from each video.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

References

  1. Book
    1. Mishkin M
    2. Malamut B
    3. Bachevalier J
    (1984)
    Memories and habits: two neural systems
    In: Lynch G, McGaugh N, editors. .Neurobiology of Learning and Memory. Guilford Press. pp. 65–77.
  2. Book
    1. Olds J
    (1977)
    Drives and Reinforcements: Behavioral Studies of Hypothalamic Functions
    Raven Press.
  3. Conference
    1. Skaggs WE
    2. McNaughton BL
    3. Gothard KM
    (1993)
    An information-theoretic approach to deciphering the hippocampal code
    Advances in neural information processing systems. pp. 1030–1037.
    1. Ursin R
    2. Ursin H
    3. Olds J
    (1966) Self-Stimulation of hippocampus in rats
    Journal of Comparative and Physiological Psychology 61:353–359.
    https://doi.org/10.1037/h0023253

Decision letter

  1. Kate M Wassum
    Senior and Reviewing Editor; University of California, Los Angeles, United States
  2. Kei Igarashi
    Reviewer; University of California, Irvine, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting the paper "Elucidating a locus coeruleus-hippocampal dopamine pathway for operant reinforcement" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Kei Igarashi (Reviewer #2).

Comments to the Authors:

We are sorry to say that, after consultation with the reviewers, we have decided that this work will not be considered further for publication by eLife.

As you will see below, all the reviewers thought that this work contains significant findings that are of interest to the neuroscience community. In particular, the finding of the reinforcing effect of locus coeruleus (LC) neurons projecting to the dentate gyrus (DG) is potentially of great interest. However, the reviewers found several substantive issues that limit the validity of the conclusions. Specifically, the reviewers are concerned about a lack of experiments to establish the release of dopamine (or still potentially norepinephrine) from LC neurons projecting to DG. Currently, this conclusion was made using intraperitoneal injections of dopamine receptor antagonist. It is important to perform local pharmacological experiments for both norepinephrine and dopamine antagonists within the DG in self-stimulating mice to fully address this. Furthermore, many experiments reported in this paper, including experiments using norepinephrine and dopamine antagonists (Figure 3F), are underpowered. The number of animals (samples) needs to be increased throughout this work, in particular, the data supporting the specificity of dopamine as opposed to norepinephrine (Figure 3F). Another important concern raised by the reviewers is that it remains unclear whether there is something unique about putative D1R-expressing neurons in the DG for controlling instrumental actions.

Overall, the reviewers thought that the above issues are essential. Although these issues could be addressed by additional experiments, it will likely take more than 2-3 months. Because of these reasons, we decided to reject this work, at least in the current form. If the authors can fully address the above issues and each of the reviewers individual concerns, we would be happy to consider a revised manuscript as a new submission.

Reviewer #1 (Recommendations for the authors):

This manuscript by Petter et al., examines the role of putative D1R expressing neurons in the dentate gyrus in regulating operant behaviors in mice. The authors show that mice will learn to press a lever to receive optogenetic stimulation of D1R-Cre targeted cells compared to eYFP control mice. Next, they show that the DG receives input from the LC as previously described. They then show that mice will self-stimulate for activation of LC neurons that project to the DG, and that optogenetic inhibition of D1-DG neurons reduces the amount of self-stimulation of LC-DG neurons. Finally, the authors use miniscope based calcium imaging to show that DG D1R expressing neurons show changes in their activity timelocked to lever pressing. Overall, the work builds upon previous studies to support a role of LC projections to the DC in instrumental learning and reinforcement. While many of the findings support these conclusions, there are a number of issues with the data and paper in its current form.

1) Targeting DG cells in the D1R-Cre mouse. The authors imply that somehow D1R expressing cells in the DG are critical for instrumental behavior. However, it is unclear whether there is robust co-localization with D1R expression and the virally targeted cells. One image is presented to suggest this, but the authors need to quantify the percent of cells that are virally targeted that express D1R vs. those that do not. This has been performed for striatal tissue, but I am not aware of studies that have done this for the DG.

2) The amount of nosepoking to receive optical stimulation in all of the self-stimulation experiments seems low, and it is somewhat difficult to really assess this as it is presented as a lever press rate. I would be helpful to present the data in the total number of nosepokes per session for all groups. Even if the level of self-stimulation is low, they do use the appropriate control group (eYFP expressing controls) show the behavior is different between the groups.

3) The tracing experiments, although simple and straightforward, suffer from low n's. 2 hemispheres from 2 mice are presented in figure 2 for example. It would be preferable if at least 3-4 biological replicates are included in each dataset. These should also include both male and female mice whenever possible.

4) The antagonist experiments presented in Figure 3 are consistent with their hypothesis that dopamine is mediating the self-stimulation effect. However, these drugs are given IP and there is no way to know whether they are actually acting in the DG vs. other areas that may also reduce reinforcement.

5) For the self-stimulation of LC neurons experiment (Figure 3) it is unclear why a non-specific viral strategy was employed. The authors could have easily targeted TH expressing neurons that project from the LC to the DG instead of only targeting neurons based on their projection target. At the very least, the authors should perform a careful quantification of the percentage of the LC cells that are targeted using the retroAAV that are TH+.

6) The presentation of the miniscope data could use some improvement. For example, how many cells for each animal were recorded? How many trials were analyzed per session? All of the image data looks processed, and it would be nice to see some raw data presented alongside the processed data. I was also struck by how short the recording sessions were (10 minutes). Why did the authors record for such a short period of time?

Reviewer #2 (Recommendations for the authors):

In this manuscript, Petter and colleagues identify the neuromodulatory pathway from the locus coeruleus to the dentate gyrus of the hippocampus. The manuscript has a novelty on focusing in this previously undescribed circuit. However, I have several major concerns that some of the claims are not justified by the results.

1. I have a concern on the propranolol experiment (Figure 3F). Although the authors state that they did not see significant effect with propranolol, there is a trend of decreases (as mentioned by the author). The sample number here looks like n=5, which would not be achieving a sufficient power. Thus, it may be the case that this circuit functions using both dopamine AND norepinephrine.

2. I have a difficulty in interpreting LC-stimulation + DG inhibition experiment (Figure 4), as this can be interpreted with many possibilities. Rather, LC axon inhibition would achieve simple conclusion about the involvement of this circuit.Reviewer #3 (Recommendations for the authors):

In this study, the authors show that stimulation of Drd1-expressing dentate gyrus (DG) neurons in the mammalian brain promotes operant reinforcement, thereby expanding the role of the hippocampus traditionally studied with respect to episodic/spatial memory to instrumental learning. This is a very interesting finding, supported by a substantial array of behavioural observations. Yet, I do have three sets of comments/questions regarding the selectivity of the main observations, and thus the strength of the related conclusions.

First, the authors conclude from their experiments that D1-expressing DG cells constitute a specific population of hippocampal neurons that supports operant reinforcement learning. But how cell-type selective is this behavioural contribution? Notably, can Drd1-non-expressing DG neurons also be involved in this behavioural effect? That is, can self-stimulation be achieved with other DG neuron types? Interestingly, many retro-Cre labelled LC neurons that project to DG are not expressing tyrosine hydroxylase (see Table 1). Likewise, is this behavioural effect selective to LC-to-DG inputs? That is, can this effect be obtained with any input targeting Drd1-expressing DG neurons (see Supplementary Figure 1)?

Second, while the data supporting the direct contribution of the LC-to-DG pathway in operant reinforcement is convincing (irrespective of whether or not such a contribution is selective), the dopaminergic identity of this pathway remains elusive. Throughout the manuscript the authors keep making this sort of strong statement: e.g., "surprisingly, these neurons receive dopaminergic projections from the locus coeruleus …" (e.g., in the Abstract). As far as I understand the results, neither the release of dopamine from, nor the dopaminergic identity of, the LC inputs to DG are demonstrated. This claim is central to the work but seems to rely on two indirect observations. First, DG-targeting LC neurons are immuno-positive for tyrosine hydroxylase. Indeed, because this marks catecholamine neurons and is thus expressed by both dopamine and norepinephrine neurons. Second, systemic injections of the β-adrenoceptor antagonist propranolol does not prevent self-stimulation while the D1-antagonist SCH 23390 does. But the direct blockade of the LC-DG pathway is not established in these pharmacological experiments. Should other pathways be recruited in this behaviour (e.g., involving VTA or SNC to other, non-hippocampal circuits), then the current interpretation of this pharmacological blockade would be misleading.

Finally, hippocampal activity is strongly coupled to animal's speed (and thus the corresponding network states that report active exploration versus immobility). The observation that D1+ DG neurons are modulated by lever pressing during operant conditioning is interesting. The inclusion of the passive reward delivery is a good control. But speed should be formally controlled for (analyses in Figure 5 and 6). This can be done in at least two ways: by using speed-matched time windows across the two reward delivery conditions (i.e., active lever press versus passive delivery), and by reporting the measure of DG activity (Δ F/F) as a function of speed at the active reward delivery.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Elucidating a locus coeruleus-hippocampal dopamine pathway for operant reinforcement" for further consideration by eLife. Your revised article has been evaluated by Kate Wassum (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As you can see, we very much appreciated your thorough response to the prior review. The manuscript is much improved. However, there are some remaining concerns that need to be addressed. These are summarized for your convenience below but are described in more detail in the reviewers' comments. In your revision please provide a point x point response to each reviewer's comment.

– There are some concerns, especially regarding Figure 3, whether drug effects on behavior are specific to reinforcement and not confounded by general effects on motor behavior. We feel this could be ideally addressed experimentally, though there may be other avenues to alleviate this concern.

– There are concerns that the conclusions that the reinforcement effects are mediated by dopamine and not norepinephrine hinge on the strong conclusions made about null results from experiments that could be underpowered (e.g., N=5) and also have high variability. As important conclusions rely on these findings, we think this concern should be addressed with additional experiments to increase power.

– Reviewer 3 makes a number of additional important points that we all agree should be addressed.

Reviewer #1 (Recommendations for the authors):

Most of my previous concerns are now resolved.

Reviewer #2 (Recommendations for the authors):

All of my previous concerns have been addressed.

Reviewer #3 (Recommendations for the authors):

1. There are concerns with some of the reinforcement data that clouds interpretation. The biggest concern I have is in Figure 3 where it is not possible to tell whether responding during the drug treatment challenges is truly being reinforced by LC-DG stimulation in these tests. These tests were run after 8d of extinction of a previous operant response, and there is no difference in responding between EXT day 8 and test day 1. In fact, they respond less during the testing. In figure 4, basal response rates are very low at the vehicle (10 responses/30min). Again, these rates are lower than extinction levels previously established. Are the authors sure that the drug effects on behavior are reinforcement-specific and not just general effects on motor behavior?

2. The only evidence that supports their claim that this is mediated by dopamine and not norepinephrine is the microinjection study, which is quite underpowered and also involves the question of reinforcement (above). This hinges on the strong conclusions made about null results from an underpowered set of experiments. For the authors are trying to rule out the contributions of NE, which is hard to do given n=5 where the mean effect size is a reduction of ~5-6 responses vs. ~2-3 responses on a task with fairly high variance to begin with. In particular, the 21nM propranolol group may be lower than the vehicle if the outlier with the increased responding is removed. This is important as the conclusion of the manuscript is that an LC-DG circuit mediates reinforcement via a dopamine input

3. It is not clear why in Figure 3 they didn't use a TH-specific viral strategy here. Since the manipulation in not cell-type specific, they really should have quantified these neurons. How many of the ChR2+ neurons are TH+?

4. The imaging studies were conducted on D1+ neurons, but not demonstrably related to LC inputs. With such small cell numbers activated here, and such a small TH+ population of LC neurons projecting to the DG – this is potentially a very small group of cells and the authors have not linked these responses to this specific input.

5. The discussion overstates a large number of the conclusions.

a. The authors point out in the text that it is interesting that this behavior doesn't really extinguish… yet this observation is left entirely unexplored. Perhaps, this circuitry is important for the acquisition of [reward-related] memories… but perhaps it is not critical to the maintenance of conditioned behavior. Some inhibition (or DA depletion) studies may give some insight into this possibility

b. Authors write: "These findings suggest that the LC supplies the primary dopaminergic input to the dorsal hippocampus (1), especially to a population of D1+ neurons in the DG (2), and that this dopaminergic pathway plays a critical role in operant reinforcement (3)."

i. It is not clearly demonstrated here that the LC provides dopaminergic input to the DG; however, it has been shown more clearly in previous studies. This language could be softened to suggest that they have data to support previous work.

ii. There is no direct evidence that LC projections modulate the D1+ population, so this may need to be softened as well.

iii. There is no evidence that this pathway is necessary, or even involved in natural reinforcement. This would require some circuit inhibition studies or pharmacological manipulations during sucrose reinforcement. It's also not clear what the authors believe the function of D1+ DG neurons to be.

c. Authors note "These findings suggest that the hippocampus contributes to the reinforcement of specific instrumental actions.". While this is possible, there is no clear evidence for this function – only evidence the calcium events were organized around lever presses. What about lever presses prior to learning? What about lever presses after extinction?

6. Is figure 1C showing a response isolated from an FR5 (as stated in the legend)? Were there no other responses within the 15-second timeframe of each response? How were these signals isolated and how was it ensured that the same data traces weren't being averaged several times but slightly offset from each other because of the temporal relationship between responses?

7. The response and the reinforcer delivery are only separated by 1 second… so it is challenging to distinguish whether there is a calcium response to reward (+1s) or not since fluorescence is already elevated at the time of reward consumption.

8. Similarly, the lever protracted 2sec after reward delivery – are the animals still consuming the sucrose pellet at this time?

9. Ultimately, it's unclear how they were able to categorize the neurons given the condensed task parameters and high variability exact spike time for the neurons shown in Figure 7B.

https://doi.org/10.7554/eLife.83600.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

As you will see below, all the reviewers thought that this work contains significant findings that are of interest to the neuroscience community. In particular, the finding of the reinforcing effect of locus coeruleus (LC) neurons projecting to the dentate gyrus (DG) is potentially of great interest. However, the reviewers found several substantive issues that limit the validity of the conclusions. Specifically, the reviewers are concerned about a lack of experiments to establish the release of dopamine (or still potentially norepinephrine) from LC neurons projecting to DG. Currently, this conclusion was made using intraperitoneal injections of dopamine receptor antagonist. It is important to perform local pharmacological experiments for both norepinephrine and dopamine antagonists within the DG in self-stimulating mice to fully address this.

We have performed local pharmacological experiments using norepinephrine and dopamine antagonists within the DG (Figures 3-4). The results support our original conclusions.

Furthermore, many experiments reported in this paper, including experiments using norepinephrine and dopamine antagonists (Figure 3F), are underpowered. The number of animals (samples) needs to be increased throughout this work, in particular, the data supporting the specificity of dopamine as opposed to norepinephrine (Figure 3F).

We have also increased the number of animals for pharmacological experiments as well as tracing experiments.

Another important concern raised by the reviewers is that it remains unclear whether there is something unique about putative D1R-expressing neurons in the DG for controlling instrumental actions.

We have included discussion of this issue (see discussion) – “Future work will have to address whether this self-stimulation effect requires D1+ neurons or if it is a property of hippocampal neurons in general.”

Reviewer #1 (Recommendations for the authors):

This manuscript by Petter et al., examines the role of putative D1R expressing neurons in the dentate gyrus in regulating operant behaviors in mice. The authors show that mice will learn to press a lever to receive optogenetic stimulation of D1R-Cre targeted cells compared to eYFP control mice. Next, they show that the DG receives input from the LC as previously described. They then show that mice will self-stimulate for activation of LC neurons that project to the DG, and that optogenetic inhibition of D1-DG neurons reduces the amount of self-stimulation of LC-DG neurons. Finally, the authors use miniscope based calcium imaging to show that DG D1R expressing neurons show changes in their activity timelocked to lever pressing. Overall, the work builds upon previous studies to support a role of LC projections to the DC in instrumental learning and reinforcement. While many of the findings support these conclusions, there are a number of issues with the data and paper in its current form.

1) Targeting DG cells in the D1R-Cre mouse. The authors imply that somehow D1R expressing cells in the DG are critical for instrumental behavior. However, it is unclear whether there is robust co-localization with D1R expression and the virally targeted cells. One image is presented to suggest this, but the authors need to quantify the percent of cells that are virally targeted that express D1R vs. those that do not. This has been performed for striatal tissue, but I am not aware of studies that have done this for the DG.

We used RNAscope to quantify the number of virally target cells that colocalized with D1R expression. We found robust colocalization (Figure 6).

2) The amount of nosepoking to receive optical stimulation in all of the self-stimulation experiments seems low, and it is somewhat difficult to really assess this as it is presented as a lever press rate. I would be helpful to present the data in the total number of nosepokes per session for all groups. Even if the level of self-stimulation is low, they do use the appropriate control group (eYFP expressing controls) show the behavior is different between the groups.

Lever pressing was used. We now include total number of presses for all data.

3) The tracing experiments, although simple and straightforward, suffer from low n's. 2 hemispheres from 2 mice are presented in figure 2 for example. It would be preferable if at least 3-4 biological replicates are included in each dataset. These should also include both male and female mice whenever possible.

We added 2 mice with retro-Cre injected into the DG of Ai-14 mice (n = 4).

4) The antagonist experiments presented in Figure 3 are consistent with their hypothesis that dopamine is mediating the self-stimulation effect. However, these drugs are given IP and there is no way to know whether they are actually acting in the DG vs. other areas that may also reduce reinforcement.

We have performed the same experiments using local infusion of these drugs (Figure 4). Similar results were found.

5) For the self-stimulation of LC neurons experiment (Figure 3) it is unclear why a non-specific viral strategy was employed. The authors could have easily targeted TH expressing neurons that project from the LC to the DG instead of only targeting neurons based on their projection target. At the very least, the authors should perform a careful quantification of the percentage of the LC cells that are targeted using the retroAAV that are TH+.

We added quantification of the percentage of LC cells that are TH+ (Figure 2) and found that ~40% of the retro-cre labeled cells are also TH+.

6) The presentation of the miniscope data could use some improvement. For example, how many cells for each animal were recorded? How many trials were analyzed per session? All of the image data looks processed, and it would be nice to see some raw data presented alongside the processed data. I was also struck by how short the recording sessions were (10 minutes). Why did the authors record for such a short period of time?

We updated the Figure to reflect how many neurons were recorded per animal and included a table (Table 2) to show the number of neurons recorded from each animal for each task. We added more imaging data from other tasks that show the number of presses (“trials”) per session for each animal (Figure 6D, Figure 7A). Sessions were short because we wanted to avoid photobleaching.

Reviewer #2 (Recommendations for the authors):

In this manuscript, Petter and colleagues identify the neuromodulatory pathway from the locus coeruleus to the dentate gyrus of the hippocampus. The manuscript has a novelty on focusing in this previously undescribed circuit. However, I have several major concerns that some of the claims are not justified by the results.

1. I have a concern on the propranolol experiment (Figure 3F). Although the authors state that they did not see significant effect with propranolol, there is a trend of decreases (as mentioned by the author). The sample number here looks like n=5, which would not be achieving a sufficient power. Thus, it may be the case that this circuit functions using both dopamine AND norepinephrine.

We added animals to bring the sample number to N=8 for the systemic injections (Figure 3). We also performed local infusions with both NE and DA antagonists (cannulae in DG) to further confirm our effects.

2. I have a difficulty in interpreting LC-stimulation + DG inhibition experiment (Figure 4), as this can be interpreted with many possibilities. Rather, LC axon inhibition would achieve simple conclusion about the involvement of this circuit.

We removed this data, as it could be difficult to interpret.

Reviewer #3 (Recommendations for the authors):

In this study, the authors show that stimulation of Drd1-expressing dentate gyrus (DG) neurons in the mammalian brain promotes operant reinforcement, thereby expanding the role of the hippocampus traditionally studied with respect to episodic/spatial memory to instrumental learning. This is a very interesting finding, supported by a substantial array of behavioural observations. Yet, I do have three sets of comments/questions regarding the selectivity of the main observations, and thus the strength of the related conclusions.

First, the authors conclude from their experiments that D1-expressing DG cells constitute a specific population of hippocampal neurons that supports operant reinforcement learning. But how cell-type selective is this behavioural contribution? Notably, can Drd1-non-expressing DG neurons also be involved in this behavioural effect? That is, can self-stimulation be achieved with other DG neuron types? Interestingly, many retro-Cre labelled LC neurons that project to DG are not expressing tyrosine hydroxylase (see Table 1). Likewise, is this behavioural effect selective to LC-to-DG inputs? That is, can this effect be obtained with any input targeting Drd1-expressing DG neurons (see Supplementary Figure 1)?

We focused on the role of D1+ neurons and the DA projections from LC. We did not examine the role of other cell types in the DG. Future work will have to investigate how specific the effects are to D1 neurons. We found ~41% of the retrogradely labeled neurons colocalized with TH.

Second, while the data supporting the direct contribution of the LC-to-DG pathway in operant reinforcement is convincing (irrespective of whether or not such a contribution is selective), the dopaminergic identity of this pathway remains elusive. Throughout the manuscript the authors keep making this sort of strong statement: e.g., "surprisingly, these neurons receive dopaminergic projections from the locus coeruleus …" (e.g., in the Abstract). As far as I understand the results, neither the release of dopamine from, nor the dopaminergic identity of, the LC inputs to DG are demonstrated. This claim is central to the work but seems to rely on two indirect observations. First, DG-targeting LC neurons are immuno-positive for tyrosine hydroxylase. Indeed, because this marks catecholamine neurons and is thus expressed by both dopamine and norepinephrine neurons. Second, systemic injections of the β-adrenoceptor antagonist propranolol does not prevent self-stimulation while the D1-antagonist SCH 23390 does. But the direct blockade of the LC-DG pathway is not established in these pharmacological experiments. Should other pathways be recruited in this behaviour (e.g., involving VTA or SNC to other, non-hippocampal circuits), then the current interpretation of this pharmacological blockade would be misleading.

As mentioned above, we also included experiments using local infusion of antagonists to address this problem.

Finally, hippocampal activity is strongly coupled to animal's speed (and thus the corresponding network states that report active exploration versus immobility). The observation that D1+ DG neurons are modulated by lever pressing during operant conditioning is interesting. The inclusion of the passive reward delivery is a good control. But speed should be formally controlled for (analyses in Figure 5 and 6). This can be done in at least two ways: by using speed-matched time windows across the two reward delivery conditions (i.e., active lever press versus passive delivery), and by reporting the measure of DG activity (Δ F/F) as a function of speed at the active reward delivery.

In order to address these concerns, we report the measure of DG activity (df/f) as a function of speed during the reward delivery period. Specifically, we used a 4 second window (+/- 2 seconds) centered on reward delivery. For each trial and neuron pair, we take the average df/f in this 4 second window and plot it as a function of speed. In order to compare neurons on the same scale df/f was normalized by the maximum df/f for each neuron.

The data is plotted for all three tasks in Author response image 1. There is no significant speed modulation for the non-contingent task (R2=0.0591, p value: 0.2924) or the delay task (R2 = 0.0311 , p value: 0.4102). The FR5 task shows a weak negative relationship between speed and df/f (R2 = -0.094 , p value: 0.0031)

Author response image 1

[Editors’ note: what follows is the authors’ response to the second round of review.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As you can see, we very much appreciated your thorough response to the prior review. The manuscript is much improved. However, there are some remaining concerns that need to be addressed. These are summarized for your convenience below but are described in more detail in the reviewers' comments. In your revision please provide a point x point response to each reviewer's comment.

– There are some concerns, especially regarding Figure 3, whether drug effects on behavior are specific to reinforcement and not confounded by general effects on motor behavior. We feel this could be ideally addressed experimentally, though there may be other avenues to alleviate this concern.

We analyzed total distance traveled for each drug condition and did not find effects on motor behavior (Figure 4F).

– There are concerns that the conclusions that the reinforcement effects are mediated by dopamine and not norepinephrine hinge on the strong conclusions made about null results from experiments that could be underpowered (e.g., N=5) and also have high variability. As important conclusions rely on these findings, we think this concern should be addressed with additional experiments to increase power.

We have performed additional experiments to increase N.

Reviewer #3 (Recommendations for the authors):

1. There are concerns with some of the reinforcement data that clouds interpretation. The biggest concern I have is in Figure 3 where it is not possible to tell whether responding during the drug treatment challenges is truly being reinforced by LC-DG stimulation in these tests. These tests were run after 8d of extinction of a previous operant response, and there is no difference in responding between EXT day 8 and test day 1. In fact, they respond less during the testing. In figure 4, basal response rates are very low at the vehicle (10 responses/30min). Again, these rates are lower than extinction levels previously established. Are the authors sure that the drug effects on behavior are reinforcement-specific and not just general effects on motor behavior?

We believe that the drug effects on behavior are reinforcement specific. The animals used for LC-hippocampal pathway stimulation with intrahippocampal cannula were all naïve to start the experiments. These animals showed an increase in responding compared to controls (Figure 4C) and significant decreases in lever pressing in response to SCH23390 but not propranolol (Figure 4D,E). Further, we analyzed the movement of these animals using DeepLabCut and found that the head movement of these animals was unchanged by the drug infusions (Figure 4F). The change in lever pressing but not gross movement suggests these are likely reinforcement specific.

2. The only evidence that supports their claim that this is mediated by dopamine and not norepinephrine is the microinjection study, which is quite underpowered and also involves the question of reinforcement (above). This hinges on the strong conclusions made about null results from an underpowered set of experiments. For the authors are trying to rule out the contributions of NE, which is hard to do given n=5 where the mean effect size is a reduction of ~5-6 responses vs. ~2-3 responses on a task with fairly high variance to begin with. In particular, the 21nM propranolol group may be lower than the vehicle if the outlier with the increased responding is removed. This is important as the conclusion of the manuscript is that an LC-DG circuit mediates reinforcement via a dopamine input

We performed additional experiments and increased the N in each group to 8.

3. It is not clear why in Figure 3 they didn't use a TH-specific viral strategy here. Since the manipulation in not cell-type specific, they really should have quantified these neurons. How many of the ChR2+ neurons are TH+?

We did quantify the number of ChR2+ neurons that are TH+ in figure 2H.

4. The imaging studies were conducted on D1+ neurons, but not demonstrably related to LC inputs. With such small cell numbers activated here, and such a small TH+ population of LC neurons projecting to the DG – this is potentially a very small group of cells and the authors have not linked these responses to this specific input.

The imaging experiments merely attempted to show the properties of D1+ neurons in the DG during operant behavior. Whether these neurons receive TH+ LC projections cannot be determined, as doing pathway specific stimulation while imaging is not technically feasible. In the paper we did not make any specific claims about the relationship between TH+ LC projections and neurons we imaged. We do know from previous work that TH+ axons strongly innervate the hippocampus (Kempadoo et al., Kaufman et al.,2020, Takeuchi et al., 2016). While we have not linked the responses of the cells directly to LC activity, we find it probably that the LC modulates these cells based on the changes in behavior with D1-DG activation of LC-DG activation.

5. The discussion overstates a large number of the conclusions.

a. The authors point out in the text that it is interesting that this behavior doesn't really extinguish… yet this observation is left entirely unexplored. Perhaps, this circuitry is important for the acquisition of [reward-related] memories… but perhaps it is not critical to the maintenance of conditioned behavior. Some inhibition (or DA depletion) studies may give some insight into this possibility

Testing reward-related memories is beyond the scope of these studies. We agree it would be interesting for future studies to inhibit D1-DG neurons during acquisition and extinction of conditioned behavior.

b. Authors write: "These findings suggest that the LC supplies the primary dopaminergic input to the dorsal hippocampus (1), especially to a population of D1+ neurons in the DG (2), and that this dopaminergic pathway plays a critical role in operant reinforcement (3)."

i. It is not clearly demonstrated here that the LC provides dopaminergic input to the DG; however, it has been shown more clearly in previous studies. This language could be softened to suggest that they have data to support previous work.

We softened the language to reflect that this claim about DA inputs to the DG is shown by previous work, rather than our findings.

ii. There is no direct evidence that LC projections modulate the D1+ population, so this may need to be softened as well.

We have revised our discussion to point out the lack of direct evidence.

iii. There is no evidence that this pathway is necessary, or even involved in natural reinforcement. This would require some circuit inhibition studies or pharmacological manipulations during sucrose reinforcement. It's also not clear what the authors believe the function of D1+ DG neurons to be.

We have revised our discussion to point out the lack of direct evidence, but our calcium imaging data shows systematic changes in press-related activity during training.

c. Authors note "These findings suggest that the hippocampus contributes to the reinforcement of specific instrumental actions.". While this is possible, there is no clear evidence for this function – only evidence the calcium events were organized around lever presses. What about lever presses prior to learning? What about lever presses after extinction?

An additional piece of evidence supporting the role of these hippocampal neurons in operant conditioning is that there are more neurons responsive to lever pressing compared to noncontingent reward delivery. There are no lever presses prior to learning in such a task, since lever pressing is learned.

6. Is figure 1C showing a response isolated from an FR5 (as stated in the legend)? Were there no other responses within the 15-second timeframe of each response? How were these signals isolated and how was it ensured that the same data traces weren't being averaged several times but slightly offset from each other because of the temporal relationship between responses?

In figure 5C there was a typo and the example neuron was actually taken from an FR1 session.

We have updated the legend to reflect this. There are no other responses within the time frame.

7. The response and the reinforcer delivery are only separated by 1 second… so it is challenging to distinguish whether there is a calcium response to reward (+1s) or not since fluorescence is already elevated at the time of reward consumption.

This is partially addressed by the non-contingent reward task, where there is no operant conditioning component.

8. Similarly, the lever protracted 2sec after reward delivery – are the animals still consuming the sucrose pellet at this time?

It is possible that the animals are still consuming the sucrose pellet at this time. If they are still consuming the pellet they can wait to respond on the lever. The lever does not retract after a fixed amount of time, and therefore the animal can press it whenever it is ready.

9. Ultimately, it's unclear how they were able to categorize the neurons given the condensed task parameters and high variability exact spike time for the neurons shown in Figure 7B.

We used a non-contingent reward task so we could examine how many cells were responsive when no operant behavior was required for a reward. In this task we found significantly fewer modulated neurons compared to a task where lever pressing was required.

https://doi.org/10.7554/eLife.83600.sa2

Article and author information

Author details

  1. Elijah A Petter

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  2. Isabella P Fallon

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1456-4152
  3. Ryan N Hughes

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4999-0215
  4. Glenn DR Watson

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  5. Warren H Meck

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  6. Francesco Paolo Ulloa Severino

    1. Department of Psychology and Neuroscience, Duke University, Durham, United States
    2. Department of Cell Biology, Duke University School of Medicine, Durham, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3725-9713
  7. Henry H Yin

    1. Department of Psychology and Neuroscience, Duke University, Durham, United States
    2. Department of Neurobiology, Duke University School of Medicine, Durham, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    hy43@duke.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1546-6850

Funding

National Institute on Drug Abuse (DA040701)

  • Henry H Yin

National Institute of Neurological Disorders and Stroke (NS094754)

  • Henry H Yin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by NIH grants DA040701 and NS094754 to HHY.

Ethics

All experimental procedures were conducted in accordance with standard ethical guidelines and were approved by the Duke University Institutional Animal Care and Use Committee (protocol number: 162-22-09).

Senior and Reviewing Editor

  1. Kate M Wassum, University of California, Los Angeles, United States

Reviewer

  1. Kei Igarashi, University of California, Irvine, United States

Version history

  1. Received: September 20, 2022
  2. Preprint posted: October 17, 2022 (view preprint)
  3. Accepted: April 20, 2023
  4. Accepted Manuscript published: April 21, 2023 (version 1)
  5. Version of Record published: May 5, 2023 (version 2)

Copyright

© 2023, Petter et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,790
    Page views
  • 301
    Downloads
  • 6
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Elijah A Petter
  2. Isabella P Fallon
  3. Ryan N Hughes
  4. Glenn DR Watson
  5. Warren H Meck
  6. Francesco Paolo Ulloa Severino
  7. Henry H Yin
(2023)
Elucidating a locus coeruleus-dentate gyrus dopamine pathway for operant reinforcement
eLife 12:e83600.
https://doi.org/10.7554/eLife.83600

Share this article

https://doi.org/10.7554/eLife.83600

Further reading

    1. Neuroscience
    Peibo Xu, Jian Peng ... Yuejun Chen
    Research Article

    Deciphering patterns of connectivity between neurons in the brain is a critical step toward understanding brain function. Imaging-based neuroanatomical tracing identifies area-to-area or sparse neuron-to-neuron connectivity patterns, but with limited throughput. Barcode-based connectomics maps large numbers of single-neuron projections, but remains a challenge for jointly analyzing single-cell transcriptomics. Here, we established a rAAV2-retro barcode-based multiplexed tracing method that simultaneously characterizes the projectome and transcriptome at the single neuron level. We uncovered dedicated and collateral projection patterns of ventromedial prefrontal cortex (vmPFC) neurons to five downstream targets and found that projection-defined vmPFC neurons are molecularly heterogeneous. We identified transcriptional signatures of projection-specific vmPFC neurons, and verified Pou3f1 as a marker gene enriched in neurons projecting to the lateral hypothalamus, denoting a distinct subset with collateral projections to both dorsomedial striatum and lateral hypothalamus. In summary, we have developed a new multiplexed technique whose paired connectome and gene expression data can help reveal organizational principles that form neural circuits and process information.

    1. Neuroscience
    Maureen van der Grinten, Jaap de Ruyter van Steveninck ... Yağmur Güçlütürk
    Tools and Resources

    Blindness affects millions of people around the world. A promising solution to restoring a form of vision for some individuals are cortical visual prostheses, which bypass part of the impaired visual pathway by converting camera input to electrical stimulation of the visual system. The artificially induced visual percept (a pattern of localized light flashes, or ‘phosphenes’) has limited resolution, and a great portion of the field’s research is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is non-invasive functional evaluation in sighted subjects or with computational models by using simulated prosthetic vision (SPV) pipelines. An important challenge in this approach is to balance enhanced perceptual realism, biologically plausibility, and real-time performance in the simulation of cortical prosthetic vision. We present a biologically plausible, PyTorch-based phosphene simulator that can run in real-time and uses differentiable operations to allow for gradient-based computational optimization of phosphene encoding models. The simulator integrates a wide range of clinical results with neurophysiological evidence in humans and non-human primates. The pipeline includes a model of the retinotopic organization and cortical magnification of the visual cortex. Moreover, the quantitative effects of stimulation parameters and temporal dynamics on phosphene characteristics are incorporated. Our results demonstrate the simulator’s suitability for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioral experiments. The modular and open-source software provides a flexible simulation framework for computational, clinical, and behavioral neuroscientists working on visual neuroprosthetics.