Successfully navigating through an environment requires internally guided behaviors and the use of external cues to make decisions and drive goal-directed actions. Decision-making is a complex and multifaceted process, many aspects of which can be interrogated in rodents. Critical components of the decision process include detecting and encoding stimuli, forming and maintaining action plans, preparing and executing an action, and comparing predicted outcomes to the actual results of that decision. Action planning is the neural representation of an intended movement and can be internally generated or externally guided by relevant cues. When externally guided, a neural action plan emerges after cue onset that persists until the action is executed. One key brain region with strong action plan representation is secondary motor cortex (M2) in rodents, analogous to Brodmann’s area 6, supplementary motor area (SMA), and pre-SMA in primates. M2 is interconnected with both sensory, motor, and cognitive brain regions (Hoover and Vertes, 2007; Jeong et al., 2016; Reep et al., 1990; Reep and Corwin, 1999) and integrates information from these networks to facilitate planning, initiation, and execution of internally generated or cue-guided goal-direction actions (Barthas and Kwan, 2017; Gremel and Costa, 2013; Inagaki et al., 2018; Sul et al., 2011; Wei et al., 2019). Neurons in M2 display heterogeneous activity during decision-making and individual neurons are tuned to differentially encode distinct action plans with either excitation or active inhibition (Chandrasekaran et al., 2017; Inagaki et al., 2018; Wei et al., 2019). M2 neurons display some of the earliest choice-related activity in the fronto-striatal network (Sul et al., 2011), encoding goal-directed action plans with neural signatures such as cue-evoked firing, activity ramping, and sustained activation during delays (Inagaki et al., 2018; Murakami et al., 2014; Svoboda and Li, 2018). The separation of opposing action plans in M2 is necessary for accuracy on cue-guided tasks; direct or indirect bilateral inhibition of select action plans in M2 impairs performance on cue-guided tasks (Hanks et al., 2015; Li et al., 2016). Furthermore, targeted photostimulation of select action plans is sufficient to drive behavioral responses (Daie et al., 2021).

Investigating what influences action plan activity in M2 is critical to understand the mechanics of goal-directed actions. In addition to cognitive and motor network connections, M2 is innervated by neuromodulators including norepinephrine (NE) from the locus coeruleus (LC) in the brainstem (Agster et al., 2013; Hoover and Vertes, 2007). The LC is a small cluster of NE containing neurons that project throughout the brain and are the near exclusive source of NE to the cortex, including M2 (Aston-Jones, 2004; Moore and Bloom, 1979). Causal studies have shown a direct role for phasic NE in increasing gain for task-relevant stimuli (Clayton et al., 2004; Dayan and Yu, 2006; Servan-Schreiber et al., 1990; Vazey et al., 2018; Waterhouse et al., 1998). In this way, LC-NE facilitates decision-making by increasing the salience of task-related cues and maintaining attention. Although the anatomical connectivity between LC and M2 is well documented, whether and how NE facilitates action planning in M2 is unclear.

Importantly, there is limited evidence on whether the function of noradrenergic modulation on action planning in M2 is similar across females and males. There is strong potential for interaction in these domains as there are known sex differences in both neuromodulatory clusters that provide input to M2, including LC (Bangasser et al., 2011; Pinos et al., 2001; Valentino et al., 2012) and behavioral strategies for decision-making (Chen et al., 2021a; Chen et al., 2021b; Orsini and Setlow, 2017; Shansky, 2018).

Here we investigated how noradrenergic tone regulates anterior M2 function and action planning in female and male rats. We recorded neural activity from anterior M2 and modulated noradrenergic signaling through β-noradrenergic receptors during a simple decision-making task, two-alternative forced-choice (2AFC). We found noradrenergic blockade decreased behavioral performance and dampened the neural representations of action plans in anterior M2, specifically by decreasing the active inhibition of irrelevant action plans. Our results show the impact of noradrenergic blockade on performance and neural activity was greater in females than males. To identify a potential mechanism for these differences in β-noradrenergic sensitivity, we characterized β1- and β2-noradrenergic receptor RNA levels in M2 of male and female rats. We found increased β2-noradrenergic receptor RNA expression in females that may underlie the sex-specific sensitivity to noradrenergic regulation of action planning.

How does norepinephrine impact the ability for anterior M2 neurons to encode action plans? Here we look at noradrenergic regulation of action planning by recording from anterior M2 neurons under β-noradrenergic blockade during a 2AFC decision-making task and characterizing β-noradrenergic receptor RNA expression in anterior M2. We found that blocking β-noradrenergic signaling blunted task-relevant neural activity. Propranolol, whether directly or indirectly, decreased the information encoded in anterior M2 during the 2AFC and behaviorally decreased task engagement and performance. The degree to which these behavioral and neural deficits occurred showed a functional sex difference where females displayed greater dependence on β-noradrenergic activity for action planning and performance. Females also showed higher levels of β-noradrenergic receptor RNA within anterior M2, particularly on local interneurons. Results from this study suggest that β-noradrenergic signaling is key to maintaining neural representation of action plans necessary for optimal performance during decision-making and provides evidence for sex differences within this mechanism.

The LC-NE system has been implicated in signal detection and altering gain of task-relevant cues in the cortex. NE acting amplifies salient cue-related signals while simultaneously quieting aberrant or ‘noisy’ background activity (Clayton et al., 2004; Dayan and Yu, 2006; Servan-Schreiber et al., 1990; Vazey et al., 2018; Waterhouse et al., 1998). Our data confirms this role for NE in higher cognitive regions such as M2 and identifies that this mechanism can work specifically through β-noradrenergic receptors. The overall effect of blocking β-noradrenergic signaling, whether direct or indirect, was a reduction in the separation of preferred and nonpreferred action plans. Separation of action plans in anterior M2, which is normally sustained from cue onset through to the action, occurred briefly or not at all. Using a linear classifier, we showed that cue type could be decoded from anterior M2 neural activity after saline but decoding accuracy was lost after propranolol (Figure 3F–I). We would predict that diminished action plan encoding after propranolol would necessitate a longer period of time to complete actions or a failure to demonstrate behavioral choices. Indeed, this decreased separation of action plans, and reduced information within anterior M2, particularly in females, was associated with increases in trial omissions (Figure 1E), increased decision times (Figure 1G), and increased response times (Figure 1H). Collectively this evidence supports a strong role for noradrenergic signaling in efficient 2AFC decision-making. The role of norepinephrine is to promote neural gain and action plan representation within anterior M2 to optimize behavioral performance.

Previous studies have shown that M2 neurons display heterogeneous activity during decision-making tasks (Chandrasekaran et al., 2017; Inagaki et al., 2018; Li et al., 2016; Svoboda and Li, 2018). In particular, M2 units show preferential activation for cues/actions based on egocentric location and active inhibition for opposing cues/actions (Inagaki et al., 2018; Wei et al., 2019). Our data replicated these findings and show that these functions are consistent across sexes with similar task-related activity in anterior M2 neurons between females and males under saline. In this study, suppressing β-noradrenergic signaling altered baseline firing properties selectively in females and caused targeted deficits in neural representations of action plans. Our findings identify a specific role for global β-noradrenergic receptor activity in regulating the representation of action plans in anterior M2 during decision-making.

As M2 neurons have previously shown heterogeneous activity, it is possible that we captured neurons with different roles in decision-making, and by analyzing population activity, included neurons with ramping and discrete activity patterns. Previous studies have identified three patterns of activity in M2; firing after presentation of ‘go’ cue, ramping from cue onset to movement initiation, and firing during forced delay and movement (Svoboda and Li, 2018). While in our data ramping neural activity was not obvious when aligning the population activity to cue onset, when aligned to lever press ramping neural activity was observed. This suggests that our neural data is more tightly aligned to action planning, motor preparation, and execution than sensory integration, as previously reported. In this study by analyzing neural activity aligned to cue onset and lever pressing, we observed major effects of propranolol on the creation and maintenance of action plans even on trials where a behavioral response was omitted.

Baseline firing rates were increased after systemic propranolol in females only; task-related encoding in females was also more severely impacted than in males, highlighting a sex-specific vulnerability. Our data on differential RNA expression levels in anterior M2 provide support for propranolol acting directly within M2, with greater impact in females. There are known direct projections to M2 from the LC, the main supplier of NE to the forebrain. However, we must note that the behavioral and neural changes we observed are likely influenced by indirect and collective actions of propranolol in many regions.

Distinctions between the cognitive effects of β1 and β2 receptor activity in the brain have identified that β2 agonism improves working memory (Ramos et al., 2008) while β1 antagonism improves cognition (Ramos et al., 2005). Propranolol is a nonspecific β antagonist that has a high affinity for β1 and β2 but not β3 receptors (Schena and Caplan, 2019). Previous studies found that when administered systemically or intra-PFC, propranolol had no effect on a working memory task (Arnsten and Goldman-Rakic, 1985; Li and Mei, 1994), suggesting that the behavioral deficits found here are not due to working memory.

At baseline, animals perform high numbers of trials with high accuracy in part because 2AFC is a simple task, the animals are well trained, and the sucrose reward incentivizes high rates of performance. Propranolol significantly decreased trial initiations across the session. As the adaptive gain theory posits, aberrant levels of tonic NE release produce behavioral deficits with low tonic levels of NE resulting in behavioral disengagement (Aston-Jones, 2004; Aston-Jones and Cohen, 2005). Decreases in trial initiation after propranolol may be a result of animals not attending to the environment and being disengaged. The reduction in trial initiations after propranolol may also reflect reduced motivation or arousal. Motivational influences were not directly tested in this study and are not commonly associated with propranolol. However, on saline session in which the trial rate decreases over the session we did not identify any difference in action plan signals within anterior M2 between early and late trials. We saw an increase in ITI lever pressing after propranolol across both sexes making arousal confounds unlikely in this study.

β-Noradrenergic receptors are found throughout the rat brain. In the cortex, both β1 and β2 receptors are heavily expressed while there is only limited β3 expression (Nicholas et al., 1991; Rainbow et al., 1984; Schena and Caplan, 2019; Wanaka et al., 1989). Within M2, we identified a sex difference in noradrenergic receptor RNA distribution with higher densities of β2 RNA in females (Figure 5B). Behavior and action plan signaling in anterior M2 was more severely impacted after propranolol in females than in males. Collectively these results may indicate a greater reliance on noradrenergic regulation of action planning in females. After propranolol, anterior M2 neurons had limited ability to create and maintain appropriate action plans, as seen by the reduced separation of preferred and nonpreferred activity in females (Figures 3D and 4D), also seen to a lesser degree in males (Figures 3E and 4E). Despite sex differences in β-noradrenergic RNA expression, males were not immune to the behavioral effects of altered neural activity after β receptor antagonism. Both males and females had rightward shifts in decision time distributions. Overall, these behavioral deficits were mediated by direct and indirect β-noradrenergic dependent alterations in decision-related activity in anterior M2. While both males and females display neural and behavioral deficits after propranolol administration, the larger impact on female behavioral performance may be attributed to greater neural disruption after propranolol. We propose that higher levels of β-noradrenergic receptor RNA support a greater dependence on β-adrenoreceptors to regulate neural activity in anterior M2.

Some aspects of decision-making strategies have known sex differences (Chen et al., 2021a; Chen et al., 2021b; Orsini and Setlow, 2017; Shansky, 2018), and the LC-NE system has known sex differences in its structure, size, and stress susceptibility (Bangasser et al., 2011; Pinos et al., 2001; Valentino et al., 2012). We have identified sex differences in decision-making and M2 function with regard to their sensitivity to perturbations of noradrenergic tone. We also identified a potential mechanism for this differential sensitivity by showing that within M2 there are distinct differences in β-noradrenergic receptor RNA expression density. Females showed a higher density of β-noradrenergic receptor RNA in M2, specifically β1 receptor RNA on glutamatergic neurons and β2 receptor RNA on GABAergic neurons (Figure 5). Prior evidence has demonstrated that females are more risk-averse in their decision-making (Orsini and Setlow, 2017). Having mechanisms for enhancing GABAergic tone in the decision-making regions of the brain would facilitate a risk-averse behavioral strategy by slowing decision times to allow greater information accumulation and reducing impulsivity. In this study, we found that the reduction in accuracy in females after propranolol was driven by an increase in omissions, rather than incorrect responses reflecting this risk-averse propensity. Noradrenergic regulation of local GABAergic interneurons in M2 appears critical for separating action plans during decision-making. Intact NE signaling increases the signal from neurons encoding cue-relevant information and suppresses activity from neurons that encode alternate irrelevant action plans. With increased β-noradrenergic receptor RNA expression in M2 of females, our results provide a potential biological basis for why disrupting NE would result in more pronounced physiological and behavioral outcomes in females. This highlights the potential for functional sex differences that emerge when neuromodulatory inputs are perturbed.

Action plans in M2 are established and maintained by promoting activity in some neurons while actively suppressing others. We found that blocking β-noradrenergic signaling led to a reduction in task-relevant encoding in anterior M2. The neural representation of opposing action plans in M2 showed reduced separation, decreased magnitude, and limited duration. This disruption in anterior M2 neural activity occurred alongside delayed and impaired decision-making as seen by increased decision times, reaction times, and omitted trials. We saw a sex bias of these impacts where females were more disrupted by blockade of β-noradrenergic signaling than males. We also saw that females had increased density of β-noradrenergic receptor RNA in M2 than males, providing a potential mechanism for this bias. Results from this study indicate that propranolol, by lowering β-noradrenergic signaling, can impair focused decision-making and task-related neural activity in anterior M2. Furthermore, sex-specific interactions in noradrenergic signaling influence cognitive function. The measures of neural activity we observed indicate an important role of β-noradrenergic receptors in the maintenance and creation of an action plan during decision-making, specifically increasing the signal-to-noise of task-relevant neural representations. Future work should test the role of β receptor modulation explicitly within M2 neurons to test the direct influence on decision-making behavior.

Data analyzed during this study are included in the supporting file.

1. 1. Agster KL
   2. Mejias-Aponte CA
   3. Clark BD
   4. Waterhouse BD

   (2013) Evidence for a regional specificity in the density and distribution of noradrenergic varicosities in rat cortex

   The Journal of Comparative Neurology 521:2195–2207.

   https://doi.org/10.1002/cne.23270

   • PubMed
   • Google Scholar
2. 1. Arnsten AF
   2. Goldman-Rakic PS

   (1985) Alpha 2-adrenergic mechanisms in prefrontal cortex associated with cognitive decline in aged nonhuman primates

   Science 230:1273–1276.

   https://doi.org/10.1126/science.2999977

   • PubMed
   • Google Scholar
3. Book

   1. Aston-Jones G

   (2004)

   Locus coeruleus, A5 and A7 noradrenergic cell groups

   In: George Paxinos, editors. The Rat Nervous System. Elsevier. pp. 259–294.

   • Google Scholar
4. 1. Aston-Jones G
   2. Cohen JD

   (2005) An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance

   Annual Review of Neuroscience 28:403–450.

   https://doi.org/10.1146/annurev.neuro.28.061604.135709

   • PubMed
   • Google Scholar
5. 1. Baker JG

   (2005) The selectivity of beta-adrenoceptor antagonists at the human beta1, beta2 and beta3 adrenoceptors

   British Journal of Pharmacology 144:317–322.

   https://doi.org/10.1038/sj.bjp.0706048

   • PubMed
   • Google Scholar
6. 1. Bangasser DA
   2. Zhang X
   3. Garachh V
   4. Hanhauser E
   5. Valentino RJ

   (2011) Sexual dimorphism in locus coeruleus dendritic morphology: A structural basis for sex differences in emotional arousal

   Physiology & Behavior 103:342–351.

   https://doi.org/10.1016/j.physbeh.2011.02.037

   • PubMed
   • Google Scholar
7. 1. Barthas F
   2. Kwan AC

   (2017) Secondary motor cortex: where “sensory” meets “motor” in the rodent frontal cortex

   Trends in Neurosciences 40:181–193.

   https://doi.org/10.1016/j.tins.2016.11.006

   • PubMed
   • Google Scholar
8. 1. Chandrasekaran C
   2. Peixoto D
   3. Newsome WT
   4. Shenoy KV

   (2017) Laminar differences in decision-related neural activity in dorsal premotor cortex

   Nature Communications 8:614.

   https://doi.org/10.1038/s41467-017-00715-0

   • PubMed
   • Google Scholar
9. 1. Chen CS
   2. Ebitz RB
   3. Bindas SR
   4. Redish AD
   5. Hayden BY
   6. Grissom NM

   (2021a) Divergent strategies for learning in males and females

   Current Biology 31:39–50.

   https://doi.org/10.1016/j.cub.2020.09.075

   • PubMed
   • Google Scholar
10. 1. Chen CS
    2. Knep E
    3. Han A
    4. Ebitz RB
    5. Grissom NM

    (2021b) Sex differences in learning from exploration

    eLife 10:e69748.

    https://doi.org/10.7554/eLife.69748

    • PubMed
    • Google Scholar
11. 1. Clayton EC
    2. Rajkowski J
    3. Cohen JD
    4. Aston-Jones G

    (2004) Phasic activation of monkey locus ceruleus neurons by simple decisions in a forced-choice task

    The Journal of Neuroscience 24:9914–9920.

    https://doi.org/10.1523/JNEUROSCI.2446-04.2004

    • PubMed
    • Google Scholar
12. 1. Daie K
    2. Svoboda K
    3. Druckmann S

    (2021) Targeted photostimulation uncovers circuit motifs supporting short-term memory

    Nature Neuroscience 24:259–265.

    https://doi.org/10.1038/s41593-020-00776-3

    • PubMed
    • Google Scholar
13. 1. Dayan P
    2. Yu AJ

    (2006) Phasic norepinephrine: a neural interrupt signal for unexpected events

    Network 17:335–350.

    https://doi.org/10.1080/09548980601004024

    • PubMed
    • Google Scholar
14. 1. Gremel CM
    2. Costa RM

    (2013) Premotor cortex is critical for goal-directed actions

    Frontiers in Computational Neuroscience 7:110.

    https://doi.org/10.3389/fncom.2013.00110

    • PubMed
    • Google Scholar
15. 1. Hanks TD
    2. Kopec CD
    3. Brunton BW
    4. Duan CA
    5. Erlich JC
    6. Brody CD

    (2015) Distinct relationships of parietal and prefrontal cortices to evidence accumulation

    Nature 520:220–223.

    https://doi.org/10.1038/nature14066

    • PubMed
    • Google Scholar
16. 1. Hoover WB
    2. Vertes RP

    (2007) Anatomical analysis of afferent projections to the medial prefrontal cortex in the rat

    Brain Structure & Function 212:149–179.

    https://doi.org/10.1007/s00429-007-0150-4

    • PubMed
    • Google Scholar
17. 1. Inagaki HK
    2. Inagaki M
    3. Romani S
    4. Svoboda K

    (2018) Low-dimensional and monotonic preparatory activity in mouse anterior lateral motor cortex

    The Journal of Neuroscience 38:4163–4185.

    https://doi.org/10.1523/JNEUROSCI.3152-17.2018

    • PubMed
    • Google Scholar
18. 1. Jeong M
    2. Kim Y
    3. Kim J
    4. Ferrante DD
    5. Mitra PP
    6. Osten P
    7. Kim D

    (2016) Comparative three-dimensional connectome map of motor cortical projections in the mouse brain

    Scientific Reports 6:20072.

    https://doi.org/10.1038/srep20072

    • PubMed
    • Google Scholar
19. 1. Li BM
    2. Mei AZT

    (1994) Delayed-response deficit induced by local injection of the alpha 2-adrenergic antagonist yohimbine into the dorsolateral prefrontal cortex in young adult monkeys

    BEHAVIORAL AND NEURAL BIOLOGY 62:134–139.

    https://doi.org/10.1016/s0163-1047(05)80034-2

    • PubMed
    • Google Scholar
20. 1. Li N
    2. Daie K
    3. Svoboda K
    4. Druckmann S

    (2016) Robust neuronal dynamics in premotor cortex during motor planning

    Nature 532:459–464.

    https://doi.org/10.1038/nature17643

    • PubMed
    • Google Scholar
21. 1. Meyers EM

    (2013) The neural decoding toolbox

    Frontiers in Neuroinformatics 7:8.

    https://doi.org/10.3389/fninf.2013.00008

    • PubMed
    • Google Scholar
22. 1. Moore RY
    2. Bloom FE

    (1979) Central catecholamine neuron systems: anatomy and physiology of the norepinephrine and epinephrine systems

    Annual Review of Neuroscience 2:113–168.

    https://doi.org/10.1146/annurev.ne.02.030179.000553

    • PubMed
    • Google Scholar
23. 1. Murakami M
    2. Vicente MI
    3. Costa GM
    4. Mainen ZF

    (2014) Neural antecedents of self-initiated actions in secondary motor cortex

    Nature Neuroscience 17:1574–1582.

    https://doi.org/10.1038/nn.3826

    • PubMed
    • Google Scholar
24. 1. Nicholas AP
    2. Pieribone VA
    3. Elde R
    4. Hökfelt T

    (1991) Initial observations on the localization of mRNA for alpha and beta adrenergic receptors in brain and peripheral tissues of rat using in situ hybridization

    Molecular and Cellular Neurosciences 2:344–350.

    https://doi.org/10.1016/1044-7431(91)90065-v

    • PubMed
    • Google Scholar
25. 1. Orsini CA
    2. Setlow B

    (2017) Sex differences in animal models of decision making

    Journal of Neuroscience Research 95:260–269.

    https://doi.org/10.1002/jnr.23810

    • PubMed
    • Google Scholar
26. 1. Pinos H
    2. Collado P
    3. Rodríguez-Zafra M
    4. Rodríguez C
    5. Segovia S
    6. Guillamón A

    (2001) The development of sex differences in the locus coeruleus of the rat

    Brain Research Bulletin 56:73–78.

    https://doi.org/10.1016/s0361-9230(01)00540-8

    • PubMed
    • Google Scholar
27. 1. Rainbow TC
    2. Parsons B
    3. Wolfe BB

    (1984) Quantitative autoradiography of beta 1- and beta 2-adrenergic receptors in rat brain

    PNAS 81:1585–1589.

    https://doi.org/10.1073/pnas.81.5.1585

    • PubMed
    • Google Scholar
28. 1. Ramos BP
    2. Colgan L
    3. Nou E
    4. Ovadia S
    5. Wilson SR
    6. Arnsten AFT

    (2005) The beta-1 adrenergic antagonist, betaxolol, improves working memory performance in rats and monkeys

    Biological Psychiatry 58:894–900.

    https://doi.org/10.1016/j.biopsych.2005.05.022

    • PubMed
    • Google Scholar
29. 1. Ramos BP
    2. Colgan LA
    3. Nou E
    4. Arnsten AFT

    (2008) Beta2 adrenergic agonist, clenbuterol, enhances working memory performance in aging animals

    Neurobiology of Aging 29:1060–1069.

    https://doi.org/10.1016/j.neurobiolaging.2007.02.003

    • PubMed
    • Google Scholar
30. 1. Reep RL
    2. Goodwin GS
    3. Corwin JV

    (1990) Topographic organization in the corticocortical connections of medial agranular cortex in rats

    The Journal of Comparative Neurology 294:262–280.

    https://doi.org/10.1002/cne.902940210

    • PubMed
    • Google Scholar
31. 1. Reep RL
    2. Corwin JV

    (1999) Topographic organization of the striatal and thalamic connections of rat medial agranular cortex

    Brain Research 841:43–52.

    https://doi.org/10.1016/s0006-8993(99)01779-5

    • PubMed
    • Google Scholar
32. 1. Rennaker RL
    2. Miller J
    3. Tang H
    4. Wilson DA

    (2007) Minocycline increases quality and longevity of chronic neural recordings

    Journal of Neural Engineering 4:L1–L5.

    https://doi.org/10.1088/1741-2560/4/2/L01

    • PubMed
    • Google Scholar
33. 1. Rikhye RV
    2. Gilra A
    3. Halassa MM

    (2018) Thalamic regulation of switching between cortical representations enables cognitive flexibility

    Nature Neuroscience 21:1753–1763.

    https://doi.org/10.1038/s41593-018-0269-z

    • PubMed
    • Google Scholar
34. 1. Schena G
    2. Caplan MJ

    (2019) Everything you always wanted to know about β₃-AR* (but were afraid to ask)

    Cells 8:357.

    https://doi.org/10.3390/cells8040357

    • PubMed
    • Google Scholar
35. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez JY
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
36. 1. Servan-Schreiber D
    2. Printz H
    3. Cohen JD

    (1990) A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior

    Science 249:892–895.

    https://doi.org/10.1126/science.2392679

    • PubMed
    • Google Scholar
37. 1. Shansky RM

    (2018) Sex differences in behavioral strategies: avoiding interpretational pitfalls

    Current Opinion in Neurobiology 49:95–98.

    https://doi.org/10.1016/j.conb.2018.01.007

    • PubMed
    • Google Scholar
38. 1. Sul JH
    2. Jo S
    3. Lee D
    4. Jung MW

    (2011) Role of rodent secondary motor cortex in value-based action selection

    Nature Neuroscience 14:1202–1208.

    https://doi.org/10.1038/nn.2881

    • PubMed
    • Google Scholar
39. 1. Svoboda K
    2. Li N

    (2018) Neural mechanisms of movement planning: motor cortex and beyond

    Current Opinion in Neurobiology 49:33–41.

    https://doi.org/10.1016/j.conb.2017.10.023

    • PubMed
    • Google Scholar
40. 1. Valentino RJ
    2. Reyes B
    3. Van Bockstaele E
    4. Bangasser D

    (2012) Molecular and cellular sex differences at the intersection of stress and arousal

    Neuropharmacology 62:13–20.

    https://doi.org/10.1016/j.neuropharm.2011.06.004

    • PubMed
    • Google Scholar
41. 1. Vazey EM
    2. Moorman DE
    3. Aston-Jones G

    (2018) Phasic locus coeruleus activity regulates cortical encoding of salience information

    PNAS 115:E9439–E9448.

    https://doi.org/10.1073/pnas.1803716115

    • PubMed
    • Google Scholar
42. 1. Wanaka A
    2. Kiyama H
    3. Murakami T
    4. Matsumoto M
    5. Kamada T
    6. Malbon CC
    7. Tohyama M

    (1989) Immunocytochemical localization of beta-adrenergic receptors in the rat brain

    Brain Research 485:125–140.

    https://doi.org/10.1016/0006-8993(89)90674-4

    • PubMed
    • Google Scholar
43. 1. Waterhouse BD
    2. Moises HC
    3. Woodward DJ

    (1998) Phasic activation of the locus coeruleus enhances responses of primary sensory cortical neurons to peripheral receptive field stimulation

    Brain Research 790:33–44.

    https://doi.org/10.1016/s0006-8993(98)00117-6

    • PubMed
    • Google Scholar
44. 1. Wei Z
    2. Inagaki H
    3. Li N
    4. Svoboda K
    5. Druckmann S

    (2019) An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability

    Nature Communications 10:216.

    https://doi.org/10.1038/s41467-018-08141-6

    • PubMed
    • Google Scholar
45. 1. Zhang Y
    2. Meyers EM
    3. Bichot NP
    4. Serre T
    5. Poggio TA
    6. Desimone R

    (2011) Object decoding with attention in inferior temporal cortex

    PNAS 108:8850–8855.

    https://doi.org/10.1073/pnas.1100999108

    • PubMed
    • Google Scholar

Author details

1. Ellen M Rodberg

   Neuroscience and Behavior Program and Department of Biology, University of Massachusetts Amherst, Amherst, United States

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4857-3970

2. Carolina R den Hartog

   Neuroscience and Behavior Program and Department of Biology, University of Massachusetts Amherst, Amherst, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

3. Emma S Dauster

   Neuroscience and Behavior Program and Department of Biology, University of Massachusetts Amherst, Amherst, United States

   Contribution

   Formal analysis, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

4. Elena M Vazey

   Neuroscience and Behavior Program and Department of Biology, University of Massachusetts Amherst, Amherst, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Visualization, Methodology, Project administration, Writing - review and editing

   For correspondence

   evazey@umass.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3311-9414

• 509

  views

• 65

  downloads

• 3

  citations

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