Changes in brain state are associated with different levels of arousal, attention, motivation, surprise, and other factors that can affect the activity patterns of large populations of cortical neurons (McAdams and Maunsell, 1999; Purcell et al., 2012; Chang et al., 2012; Falkner et al., 2013; Downer et al., 2015; Ecker et al., 2016; Thiele et al., 2016). These changes are thought to result, in part, from the widespread release of neuromodulators (Aoki et al., 1987; Devauges and Sara, 1990; Schultz et al., 1993; Dalley et al., 2001; Bouret and Sara, 2005; Aston-Jones and Cohen, 2005; McGaughy et al., 2008; Salamone et al., 2009; Pinto et al., 2013; Varazzani et al., 2015; Khani and Rainer, 2016; Minces et al., 2017). Different neuromodulatory systems have different anatomical and physiological properties (Kupfermann, 1979; Aoki et al., 1987; Xiang et al., 1998; Flores-Hernandez et al., 2000; Fernández-Pastor and Meana, 2002; Ding and Perkel, 2002; Salgado et al., 2011; Herrero et al., 2013; Sugihara et al., 2016; Doyle and Meeks, 2017) that are thought to support their different roles in neural information processing (Abbott and Dayan, 1999; Nirenberg and Latham, 2003; Averbeck et al., 2006; Silver, 2010; Beck et al., 2011; Moreno-Bote et al., 2014; Kanitscheider et al., 2015; Lin et al., 2015; Rodenkirch et al., 2019). One prominent example is the locus coeruleus (LC)-norepinephrine (NE) system, whose diffuse projections throughout the brain, close relationship to the sleep-wake cycle, and relationship to electroencephalography (EEG) and pupillometry have led to several theories of its role in arousal-related modulations of cortical activity and function (Aston-Jones and Cohen, 2005; Aston-Jones and Bloom, 1981; Nieuwenhuis et al., 2011; Gilzenrat et al., 2010; Einhäuser et al., 2010; Murphy et al., 2011; Murphy et al., 2014; Joshi et al., 2016; Joshi and Gold, 2020). However, only a small number of studies have shown direct relationships between (artificial) LC activity and cortical activity in vivo (e.g., Devilbiss and Waterhouse, 2011), limiting our understanding of the exact nature of these relationships.

Our aim was to test if and how endogenous, ongoing activity and sensory-driven, evoked responses in the LC relate to changes in neural activity patterns in the anterior cingulate cortex (ACC) of the primate brain. We targeted the ACC because it has strong reciprocal connectivity, both structural and functional, with the LC (Lewis et al., 1979; Morrison et al., 1979; Porrino and Goldman-Rakic, 1982; Jones and Olpe, 1984; Fernández-Pastor et al., 2005; Gompf et al., 2010; Chandler and Waterhouse, 2012; Chandler et al., 2013; Tervo et al., 2014; Köhler et al., 2016; De Gee et al., 2017; Koga et al., 2020). Moreover, ACC neural activity can encode computations that underlie adaptive, goal-directed behaviors, including those that are also associated with indirect measures of LC-linked arousal, such as changes in pupil size and the P300 component of the event-related potential (ERP; Shima and Tanji, 1998; Gehring and Willoughby, 2002; Holroyd and Coles, 2002; Critchley et al., 2005; Matsumoto et al., 2007; Hayden et al., 2011; Shenhav et al., 2013; Ebitz and Platt, 2015; Sarafyazd and Jazayeri, 2019). Thus, interactions between LC and ACC neural activity patterns are likely to have broad behavioral relevance.

We recorded neural activity simultaneously in LC and ACC (Figure 1) and measured the pupil size of alert monkeys under two behavioral conditions: (1) performing a fixation task and (2) performing a fixation task with randomly presented sounds. Both of these conditions have been associated with variations in arousal that covary with pupil size and can affect cognition and behavior (Aston-Jones and Bloom, 1981; Nieuwenhuis et al., 2011; Einhäuser et al., 2010; Gilzenrat et al., 2010; Murphy et al., 2011; Murphy et al., 2014; Varazzani et al., 2015; Joshi et al., 2016). Conditioned on the firing (or lack of firing) of LC neurons, we measured activity patterns of individual neurons and coordinated activity between pairs of neurons in ACC, both of which govern the information processing capacities of neural networks (Britten et al., 1992; Parker and Newsome, 1998; Cohen and Kohn, 2011; Moreno-Bote et al., 2014; Lin et al., 2015; Kohn et al., 2016). We were particularly interested in the timescales over which these features of neural activity in LC and ACC are related, which can provide insights into putative underlying mechanisms. For example, neuromodulatory effects of the LC-NE system on ACC might be expected to have a relatively long time course compared with the time course of typical synaptic events that are mediated by faster, glutamatergic neurotransmission (Feldman RS et al., 1997; McCormick and Prince, 1988; Wang and McCormick, 1993; Schmidt et al., 2013; Timmons et al., 2004). Below we show that such relatively long timescale relationships are evident between LC and ACC neural activity patterns, in particular changes in coordinated activity between pairs of ACC neurons that tend to decrease or increase in relation to spontaneous or evoked LC spikes, respectively.

Figure 1

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We analyzed neural activity from simultaneously measured sites in the LC (n = 84 single units, including 78 simultaneously recorded pairs from 35 sites in monkey Sp; 84/80/31 in monkey Ci) and ACC (372/4336/35 in monkey Sp; 275/2875/31 in monkey Ci) and from LC-only recordings (71/12/73 in monkey Ci and 36/8/34 in monkey Oz) while the monkeys maintained fixation on a visual spot. Our analyses focused on whether and how mean spiking activity and individual and pairwise neuronal variability measured in one brain region (ACC or LC) related to neuronal activity in the other brain region (LC or ACC, respectively). To understand the temporal dynamics of these potential relationships, we systematically tested for effects across a broad range of time windows. These windows ranged in duration from 100 ms, which is often used to study neural synchrony, to 1 s, which is consistent with typical timescales of slow neuromodulatory influences (Feldman RS et al., 1997; McCormick and Prince, 1988; Wang and McCormick, 1993; Schmidt et al., 2013; Timmons et al., 2004; Doiron et al., 2016). Because LC and ACC are connected reciprocally, we tested for relationships in both directions. Reliable relationships in either direction are included in the main figures, and the remaining results are shown in figure supplements.

Single-neuron activity during passive fixation

During passive fixation, LC neurons were weakly active, with no spikes measured on more than a third of all trials and otherwise a median (interquartile range [IQR]) firing rate of 1.8 [0.9–3.6] sp/s measured during 1.1 s of stable fixation (Figure 2A). For each session, we divided trials into six groups based on the given LC neuron’s firing rate: trials with firing rate = 0 (LC_zero), 1, 2, 3, and ≥4 sp/s formed five groups, and all trials with firing rate >0 formed a sixth group (LC_non-zero). We then assessed ACC neural activity measured at the same time, conditioned on the LC group.

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The mean and variance of ACC spike counts, along with their Fano factor (the ratio of variance over mean), increased steadily as a function of the duration of the counting window. These trends showed an apparent, small relationship with simultaneously measured LC spiking activity, such that the mean, variance, and Fano factor measured in the ACC were, on average, slightly higher for LC_zero versus LC_non-zero trials (Figure 2B–D). However, these relationships were not statistically reliable when considering data from both monkeys combined together and only in one case (Figure 2G) when considering data separately for each monkey (sign-rank test for H₀: median difference between LC_zero and LC_non-zero conditions computed per ACC unit, or ANOVA test for an effect of LC firing rate group, p > 0.05 in all cases; Figure 2B–G). Across ACC units, differences in Fano factor for LC_zero versus LC_non-zero trials were associated strongly with differences in spike-count variance and more weakly with differences in spike counts (Figure 2—figure supplement 1).

In addition, the mean, variance, and Fano factor of LC spike counts also increased steadily as a function of the duration of the counting window. However, none of these trends showed a statistically significant relationship to simultaneously measured ACC spiking activity (sign-rank test and ANOVA, p > 0.05 in all cases; Figure 2—figure supplement 2).

Coordinated activity during passive fixation

During passive fixation, pairs of neurons in ACC had coordinated spiking activity that varied considerably in sign and magnitude across pairs but also, as has been reported previously, depended systematically on the size of the time window used to count spikes (de la Rocha et al., 2007). Specifically, pairwise spike-count correlations (r_sc) in ACC had median [IQR] values of 0.03 [–0.01 0.07] using 200 ms counting windows and systematically increased and became more variable across pairs with larger counting windows up to 1 s (0.05 [–0.03 0.14]; ANOVA test for effect of window size, p = 0.0243).

These ACC spike-count correlations also depended on the spiking activity of LC neurons measured at the same time. Specifically, the correlations were largest when (1) they were measured using relatively large time windows; (2) for ACC pairs that exhibited relatively large, positive correlations independent of LC firing; and (3) when the simultaneously recorded LC neuron was not active (Figures 3 and 4). For the example ACC pair illustrated in Figure 3A–C, r_sc was near zero when computed using relatively small time windows and then increased steadily with increasing bin sizes. The magnitude of these increases in r_sc with bin size was larger on trials in which the simultaneously measured LC neuron was not spiking versus spiking (Figure 3C).

Figure 3

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The population of ACC pairs exhibited similar trends, with r_sc values tending to be smaller on LC_non-zero versus LC_zero trials, particularly for larger time windows and for ACC pairs with non-negative LC-independent r_sc values. To visualize these effects, we first divided ACC pairs into terciles, according to their LC-independent r_sc values (Figure 4). Under these conditions, ACC r_sc values from the upper two terciles of LC-independent r_sc values were reduced by up to ~50% when computed on trials with at least one or more LC spike versus trials with no LC spikes (Figure 4D–F). The magnitude of these reductions was not related in a consistent manner to the magnitude of LC_non-zero activity (ANOVA test for group effect of LC firing rates > 0). These relationships were not evident on shuffled trials, supporting the idea that the trial-by-trial relationships that we identified were not spurious reflections of the spiking statistics of each region considered separately (Figure 4—figure supplement 1). Furthermore, these LC-linked differences in ACC r_sc values did not result simply from changes in ACC neuron firing rates because the two measures were unrelated (Figure 4—figure supplement 2A). We found a weak but statistically reliable relationship between LC-linked changes in ACC r_sc and changes in ACC Fano factor (Figure 4—figure supplement 2B).

In contrast, spike-count correlations (r_sc) from simultaneously measured pairs of LC neurons did not depend systematically on concurrently measured ACC activity. In general, ACC-independent LC r_sc values were roughly similar to LC-independent ACC r_sc values, increasing steadily and becoming more variable as a function of the counting-window size; for example, median [IQR] values were 0.02 [0 0.09] for 200 ms windows and 0.06 [–0.02 0.19] for 1 s windows. However, these values did not show reliable differences when compared on trials with high versus low ACC firing, analyzed in the same way as the LC-linked ACC r_sc values (Figure 4—figure supplement 3). Moreover, the distributions of these ACC-linked LC r_sc values did not appear to come from the same (shifted) distribution as the LC-linked ACC r_sc values for all five time bins (Kolmogorov–Smirnov test for H₀: both sets of values come from the same distribution, p < 0.0189 in all five cases). Thus, differences in single-unit LC activity were associated with differences in coordinated activity patterns of pairs of neurons in ACC, but differences in single-unit ACC activity were not associated with differences in coordinated activity patterns of pairs of neurons in LC.

Relationship between pupil diameter, LC spiking, and ACC coordinated activity during passive fixation

To further examine relationships between coordinated activity in ACC and activation of LC-linked arousal systems, we examined both relative to changes in pupil diameter. During passive fixation, there is a quasi-periodic fluctuation of the pupil (Lowenstein and Loewenfeld, 1969; Pong and Fuchs, 2000; Joshi et al., 2016). Consistent with our previous findings (Joshi et al., 2016), the timing of LC spiking was related to the phase of these ongoing pupil fluctuations, such that LC spiking tended to be higher preceding dilation versus constriction. These modulations showed a systematic precession with dilation phase that corresponded to a delay of ~270 ms from the maximum modulation of LC activity to the relevant pupil change (Figure 5A; median of intercepts from linear regression of per-time-bin peaks of individual LC PETHs; bootstrapped 95% confidence interval from regression for each LC neuron = [177–353 ms]; for examples of pupil cycles and measurement epochs, see Figure 5—figure supplements 1 and 2, respectively).

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Combined with our current findings that pairwise correlations in ACC tended to be smaller when LC is active during passive fixation, these results predicted that pairwise correlations in ACC should also vary systematically with the phase of ongoing fluctuations in pupil diameter. We found such a relationship between pupil phase and ACC r_sc values. Specifically, over a wide range of pupil phase, decreases in ACC r_sc followed increases in LC spiking (the fitted line in panel B is shifted to the right relative to the fitted line in A; note also that increases in ACC r_sc, seen as the bright yellow band in the lower-left corner of panel B, occurred even earlier and are examined in more detail in Figure 9). These modulations showed a systematic progression with pupil phase over roughly the same time frame as LC firing, implying that modulations of both LC firing and ACC r_sc had relatively fixed temporal relationships to pupil fluctuations and therefore to each other, as well (Figure 5B; Wilcoxon rank-sum test for H₀: equal LC PETH and ACC r_sc slopes, p = 0.23).

Single-neuron activity in response to startling events

We further examined relationships between LC and ACC neural activity in the context of external events that can cause a startle response. We showed previously that a brief, loud sound played on randomly selected fixation trials (‘beep trials’) can elicit a transient pupil dilation as well as transient responses from individual LC neurons (Joshi et al., 2016; evoked responses from an example LC neuron are shown in Figure 6A, and the LC population average from three monkeys is shown in Figure 6B). The beep stimulus also elicited a consistent, albeit weaker, response in the ACC (evoked responses from an example ACC neuron are shown in Figure 6D, and the ACC population average from two monkeys is shown in Figure 6E). The averaged ACC response included a slight excitation and then inhibition relative to baseline within 200 ms of stimulus presentation. Although we found some individual cases with a biphasic response (e.g., Figure 6D), the average response likely resulted from the range of heterogeneous responses of individual ACC neurons (Figure 6—figure supplement 1).

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The startle events also caused transient reductions in the trial-to-trial variability (measured as the Fano factor) of spiking activity of individual neurons in both LC and ACC, with a slightly more sustained effect in the ACC (Figure 6C and F). This reduction in neuronal variability following the onset of a stimulus, also referred to as quenching, has been reported previously for neurons in a range of other cortical regions and for a range of task conditions (Churchland et al., 2010).

Coordinated activity in response to startling events

In the ACC, we found no evidence that the startling sound caused systematic changes in coordinated activity when measured independently of the LC response. Specifically, spike-count correlations were highly variable across ACC neuron pairs, with no statistically reliable, systematic differences when compared before versus after the beep (comparing ACC r_sc before versus after beep for spikes measured in 1 s bins; Mann–Whitney U-test, p > 0.05 for both monkeys).

In contrast, we found systematic relationships between the beep-induced changes in coordinated activity in the ACC and the simultaneously measured LC response. Specifically, LC neurons did not show a characteristic transient response on every presentation of a startle stimulus: the median fraction of trials with a response that included a transient increase and then decrease in spiking activity was 54% for monkey Sp and 43% for monkey Ci (Figure 7—figure supplement 1). Therefore, we tested if and how arousal-linked changes in ACC correlated activity depended on whether or not the simultaneously recorded LC neuron responded transiently to the startling sound. Specifically, we computed ACC r_sc in time bins of different sizes, separately for 1 s preceding and 1 s following the sound. ACC r_sc tended to be larger after versus before the beep stimulus, but only for the subset of trials in which the beep also elicited the characteristic LC response (Figure 6A and B), for both monkeys (Figure 7A). These modulations did not reflect simply transient differences in LC firing rates but rather specific differences between the presence and absence of the stimulus-evoked LC response (Figure 7B). These modulations of ACC pairwise correlations also did not reflect LC-driven differences in the stimulus-driven quenching of response variability in ACC, which was similar on trials with and without LC-evoked responses (Figure 7—figure supplement 2).

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Relationship between pupil diameter, LC spiking, and ACC coordinated activity in response to startling events

We showed previously that the magnitude of beep-evoked LC responses is correlated positively with the size of the simultaneously evoked change in pupil diameter (Joshi et al., 2016). We reproduced those results in our current data set (Figure 8—figure supplement 1). We extended those results by showing that these pupil-LC relationships also relate systematically to beep-evoked changes in ACC r_sc. Specifically, we measured beep-evoked changes in ACC r_sc separately for trials grouped by LC and pupil responses, focusing on how large or small pupil responses related to the evoked-LC versus no-evoked-LC differences in ACC r_sc shown in Figure 7. In general, the beep-evoked changes in ACC r_sc that required an LC-evoked response were larger on trials in which the evoked LC response was accompanied by a large versus small pupil dilation (Figure 8A). These effects were not evident when considering baseline, not evoked, pupil size (Figure 8B).

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Relative timing of neural activity patterns in LC and ACC

To better understand the relative timing of firing patterns (both individual neuron firing rate changes and paired-neuron r_sc changes) in LC and ACC, we measured their time courses relative to two external events with well-defined timing: onset of stable fixation on no-beep trials and onset of the sound on beep trials (Figure 9). For no-beep trials, LC firing rates increased after the fixation point turned on and before stable fixation was acquired (Figure 9A). This epoch also included elevated ACC r_sc (particularly when the simultaneously recorded LC neuron was not active, consistent with the results shown in Figure 4) that preceded the elevation in LC firing (Figure 9B, gray lines). Both LC firing and ACC r_sc tended to stabilize after stable fixation was attained. These results are consistent with the possibility that LC activation prior to fixation onset drives (or at least coincides with) a lasting decrease in ACC r_sc during the remainder of the trial.

Figure 9

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Following a beep, there was a different pattern of relationships between LC firing and ACC r_sc. Specifically, we divided trials that did (Figure 9C and D) or did not (Figure 9E and F) have a beep-evoked LC response (like in Figures 7 and 8). In general, the beep caused a transient increase in ACC r_sc, and this increase tended to occur more rapidly on trials with (Figure 9D, gold shaded area) versus without (Figure 9F, gold shaded area) a concomitant LC response (Mann–Whitney U-test, for H₀: the linear rate of increase of ACC r_sc in this period was larger when LC neuron did versus did not respond, p = 0.0297). Note that the LC transient response (Figure 9C) peaked at the beginning of the epoch in which the ACC r_sc started to increase, which is consistent with the idea that the LC-evoked response drives (or at least coincides with) a faster rate of ACC r_sc increase (steeper slope in the gold region in Figure 9D compared with F). The evoked LC response was also associated with a more reliable and larger increase in ACC r_sc later in the trial (compare Figure 9D and F; Mann–Whitney U-test for H₀: ACC r_sc 500–1000 ms after the beep was the same on trials with versus without a beep-evoked LC response, p = 0.0350 for both monkeys’ data pooled together) but was not associated with differences in ACC r_sc in the time just before the beep (i.e., ACC r_sc did not anticipate whether or not the beep would evoke an LC response; Mann–Whitney U-test for H₀: ACC r_sc –500–0 ms before the beep was the same on trials with versus without a beep-evoked LC response, p = 0.0693 for both monkeys’ data pooled together).

We measured relationships between pupil-linked activation of the LC-NE system and changes in cortical neural activity patterns. Previously, we showed that changes in pupil size during passive, near fixation and when driven by external startling events covary with the timing of spiking activity in LC and parts of brainstem and cortex, including the ACC (Joshi et al., 2016). Other studies have shown that pupil size and other indirect measures of LC activation can also correspond to changes in coordinated activity in sensory cortex under certain conditions (Reimer et al., 2014; Reimer et al., 2016; Vinck et al., 2015; McGinley et al., 2015). These previous studies did not explore the relationships between ongoing versus evoked LC activation (or NE release) and concurrent changes in both coordinated cortical activity and pupil size. We extended those findings, including one study that elicited changes in coordinated activity in cortex via manipulation of LC activity (Devilbiss and Waterhouse, 2011), by showing that LC neuronal activity is reliably associated with changes in correlated spiking activity (spike-count correlations, or r_sc) between pairs of neurons in ACC. We further showed that these LC-linked changes in ACC r_sc depend on the nature of the LC activation. During passive fixation and in the absence of controlled external inputs, ongoing LC firing was associated with a reduction in ACC r_sc. In contrast, startling sounds drove transient increases in LC firing that were associated with an increase in ACC r_sc. Under both conditions, LC and pupil-linked changes in ACC r_sc were most pronounced over relatively long time windows (>500 ms) that are consistent with neuromodulatory timescales and thus might involve the effects of LC-mediated NE release in the ACC (Feldman RS et al., 1997; McCormick and Prince, 1988; Wang and McCormick, 1993; Schmidt et al., 2013; Timmons et al., 2004).

The context dependence of these effects implies that spontaneous versus sensory-evoked activation of the LC-NE system can have different effects on coordinated patterns of activity in cortical networks. These effects seem likely to involve the different LC firing patterns under these conditions, which for sensory-evoked activity include not just a large, transient increase in spike rate but also a reduction in spike-count variability and heterogenous relationships to pupil-linked changes in arousal (Joshi et al., 2016). In principle, different temporal patterns of NE release could mediate a range of synaptic effects via distinct NE receptor subtypes with different affinities and different spatial distributions in different parts of the brain (Aoki et al., 1987; Nicholas et al., 1993; Arnsten et al., 1998; Arnsten, 2000; Berridge et al., 2012; Berridge and Spencer, 2016). These synaptic effects can influence both excitatory and inhibitory neurons as well as astrocytes, resulting in changes in network dynamics in thalamus, cortex, and elsewhere (Segal and Bloom, 1976; Dillier et al., 1978; Waterhouse et al., 1980; McCormick and Prince, 1988; Wang and McCormick, 1993; McLean and Waterhouse, 1994; Fernández-Pastor and Meana, 2002; Salgado et al., 2011; Salgado et al., 2016; Paukert et al., 2014; Guan et al., 2015; Schiemann et al., 2015; Sherpa et al., 2016; Garcia-Junco-Clemente et al., 2019; Aston-Jones and Cohen, 2005; Ohshima et al., 2017; Rodenkirch et al., 2019). Exactly how these diverse mechanisms support context-dependent changes in coordinated activity patterns merits further study, perhaps by selectively controlling the magnitude and timing of NE release in cortex while assessing changes in coordinated activity and using biophysically inspired models to test hypotheses about the underlying mechanisms (de la Rocha et al., 2007; Doiron et al., 2016).

In contrast to the relationships that we identified between LC activation and coordinated ACC activity patterns, we did not find similarly reliable relationships between the spiking activity of individual neurons measured simultaneously in each of the two brain regions. For example, we found slight increases and decreases in mean firing rates and a marked reduction in the trial-to-trial variability of firing rates of individual units in the ACC in response to external stimuli, as has been reported previously for other cortical regions (Churchland et al., 2010). However, these changes in single-unit ACC responses were not related reliably to properties of the concurrently measured LC response that we could measure and thus may involve mechanisms other than the LC-NE system. These findings do not appear to be consistent with previous work showing improvements in signal-to-noise ratios in cortex in response to LC-NE activation or during states of higher versus lower global arousal (Kolta et al., 1987; McLean and Waterhouse, 1994; Lee et al., 2018; Lombardo et al., 2018). This difference could reflect differences in the specific neurons that we targeted in ACC, the different task conditions we tested relative to previous studies, analyses based on ongoing versus evoked LC activity, or some combination of these factors, and merits further study.

What might be the function of LC- and arousal-linked changes in ACC correlations? In general, correlations in spiking activity can be useful or detrimental depending on a number of factors, many of which remain unexplored, particularly outside of sensory cortex (Averbeck et al., 2006; Cohen and Kohn, 2011; Kohn et al., 2016). For example, an increase in correlations is considered a possible mechanism for connecting neural populations over a range of spatial and temporal scales (Gray et al., 1989; Singer, 1999; Riehle et al., 1997). Some computational schemes can benefit from increases in correlated variability (Abbott and Dayan, 1999; Singer and Gray, 1995; Gray, 1999; Kohn et al., 2016; Valente et al., 2021). However, an increase in correlations can also negatively impact the information coding capacity of a large neural population, particularly over longer integration windows (Zohary et al., 1994; Bair et al., 2001; Averbeck et al., 2006; Renart et al., 2010; but also see Nirenberg and Latham, 2003 Moreno-Bote et al., 2014, for alternative interpretations). Global states such as arousal and attention (traditionally linked with LC-NE and cholinergic systems, respectively) can modulate cortical correlations (Cohen and Maunsell, 2009; Mitchell et al., 2009; Herrero et al., 2013; Schmidt et al., 2013). Some work has also suggested that correlations induced by common inputs must be actively decorrelated by the action of local recurrent excitation and inhibition to preserve information fidelity (Ecker et al., 2010; Renart et al., 2010).

Our results suggest that both increases and decreases in coordinated neural activity patterns in cortex may be under context-dependent, moment-by-moment control of the LC-NE system. Event-driven transient activation of LC could help to synchronize populations of cortical neurons by shifting them to a more correlated state. Conversely, during nonevoked, ongoing (event-independent) firing, the NE release could enhance information processing and signal-to-noise ratios by reducing cortical correlations. Further work is needed to identify if and how NE-mediated changes in network dynamics subserve these functions, particularly in the context of goal-directed behaviors that involve multiple brain regions, including LC and ACC, as well as other neuromodulator systems (Hayden et al., 2011; Varazzani et al., 2015; Ebitz and Platt, 2015; Alamia et al., 2019).

Three adult male rhesus monkeys (Macaca mulatta) were used for this study (monkeys Oz, Ci, and Sp). All training, surgery, and experimental procedures were performed in accordance with the NIH’s Guide for the Care of Use of Laboratory Animals and were approved by the University of Pennsylvania Institutional Animal Care and Use Committee (protocol 806027). The behavioral task and pupillometry recording and analysis techniques were identical to those we used previously (Joshi et al., 2016). Briefly, fixation trials were of variable length (1–5 s, uniformly distributed). The monkey was rewarded with a drop of water or diluted Kool-Aid for maintaining fixation until the end of the trial. On a subset of randomly chosen trials (~25%), after 1–1.5 s of fixation a sound (1 kHz, 0.5 s) was played over a speaker in the experimental booth (‘beep trials’). The monkey was required to maintain fixation through the presentation of the sound, until the fixation point was turned off.

Data and Matlab code for all figures in this manuscript are available at: https://github.com/TheGoldLab/LC_ACC_paper_Joshi_Gold_2021 (copy archived at swh:1:rev:22b8c94380c057201fb1fcc56c6037b70c56021c).

1. 1. Abbott LF
   2. Dayan P

   (1999) The effect of correlated variability on the accuracy of a population code

   Neural Computation 11:91–101.

   https://doi.org/10.1162/089976699300016827

   • PubMed
   • Google Scholar
2. 1. Alamia A
   2. VanRullen R
   3. Pasqualotto E
   4. Mouraux A
   5. Zenon A

   (2019) Pupil-Linked Arousal Responds to Unconscious Surprisal

   The Journal of Neuroscience 39:5369–5376.

   https://doi.org/10.1523/JNEUROSCI.3010-18.2019

   • PubMed
   • Google Scholar
3. 1. Aoki C
   2. Joh TH
   3. Pickel VM

   (1987) Ultrastructural localization of beta-adrenergic receptor-like immunoreactivity in the cortex and neostriatum of rat brain

   Brain Research 437:264–282.

   https://doi.org/10.1016/0006-8993(87)91642-8

   • PubMed
   • Google Scholar
4. 1. Arnsten AF
   2. Steere JC
   3. Jentsch DJ
   4. Li BM

   (1998) Noradrenergic influences on prefrontal cortical cognitive function: opposing actions at postjunctional alpha 1 versus alpha 2-adrenergic receptors

   Advances in Pharmacology 42:764–767.

   https://doi.org/10.1016/s1054-3589(08)60859-5

   • PubMed
   • Google Scholar
5. 1. Arnsten AF

   (2000) Through the looking glass: differential noradenergic modulation of prefrontal cortical function

   Neural Plasticity 7:133–146.

   https://doi.org/10.1155/NP.2000.133

   • PubMed
   • Google Scholar
6. 1. Aston-Jones G
   2. Bloom FE

   (1981)

   Norepinephrine-containing locus coeruleus neurons in behaving rats exhibit pronounced responses to non-noxious environmental stimuli

   The Journal of Neuroscience 1:887–900.

   • PubMed
   • Google Scholar
7. 1. Aston-Jones G
   2. Rajkowski J
   3. Kubiak P
   4. Alexinsky T

   (1994) Locus coeruleus neurons in monkey are selectively activated by attended cues in a vigilance task

   The Journal of Neuroscience 14:4467–4480.

   https://doi.org/10.1523/JNEUROSCI.14-07-04467.1994

   • PubMed
   • Google Scholar
8. 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
9. 1. Averbeck BB
   2. Latham PE
   3. Pouget A

   (2006) Neural correlations, population coding and computation

   Nature Reviews. Neuroscience 7:358–366.

   https://doi.org/10.1038/nrn1888

   • PubMed
   • Google Scholar
10. 1. Bair W
    2. Zohary E
    3. Newsome WT

    (2001) Correlated firing in macaque visual area MT: time scales and relationship to behavior

    The Journal of Neuroscience 21:1676–1697.

    https://doi.org/10.1523/JNEUROSCI.21-05-01676.2001

    • PubMed
    • Google Scholar
11. 1. Beck J
    2. Bejjanki VR
    3. Pouget A

    (2011) Insights from a simple expression for linear fisher information in a recurrently connected population of spiking neurons

    Neural Computation 23:1484–1502.

    https://doi.org/10.1162/NECO_a_00125

    • PubMed
    • Google Scholar
12. 1. Berridge CW
    2. Shumsky JS
    3. Andrzejewski ME
    4. McGaughy JA
    5. Spencer RC
    6. Devilbiss DM
    7. Waterhouse BD

    (2012) Differential sensitivity to psychostimulants across prefrontal cognitive tasks: differential involvement of noradrenergic α₁ - and α₂-receptors

    Biological Psychiatry 71:467–473.

    https://doi.org/10.1016/j.biopsych.2011.07.022

    • PubMed
    • Google Scholar
13. 1. Berridge CW
    2. Spencer RC

    (2016) Differential cognitive actions of norepinephrine a2 and a1 receptor signaling in the prefrontal cortex

    Brain Research 1641:189–196.

    https://doi.org/10.1016/j.brainres.2015.11.024

    • PubMed
    • Google Scholar
14. 1. Bouret S
    2. Sara SJ

    (2004) Reward expectation, orientation of attention and locus coeruleus-medial frontal cortex interplay during learning

    The European Journal of Neuroscience 20:791–802.

    https://doi.org/10.1111/j.1460-9568.2004.03526.x

    • PubMed
    • Google Scholar
15. 1. Bouret S
    2. Sara SJ

    (2005) Network reset: a simplified overarching theory of locus coeruleus noradrenaline function

    Trends in Neurosciences 28:574–582.

    https://doi.org/10.1016/j.tins.2005.09.002

    • PubMed
    • Google Scholar
16. 1. Bouret S
    2. Richmond BJ

    (2009) Relation of locus coeruleus neurons in monkeys to Pavlovian and operant behaviors

    Journal of Neurophysiology 101:898–911.

    https://doi.org/10.1152/jn.91048.2008

    • PubMed
    • Google Scholar
17. 1. Britten KH
    2. Shadlen MN
    3. Newsome WT
    4. Movshon JA

    (1992) The analysis of visual motion: a comparison of neuronal and psychophysical performance

    The Journal of Neuroscience 12:4745–4765.

    https://doi.org/10.1523/JNEUROSCI.12-12-04745.1992

    • PubMed
    • Google Scholar
18. 1. Chandler D
    2. Waterhouse BD

    (2012) Evidence for broad versus segregated projections from cholinergic and noradrenergic nuclei to functionally and anatomically discrete subregions of prefrontal cortex

    Frontiers in Behavioral Neuroscience 6:20.

    https://doi.org/10.3389/fnbeh.2012.00020

    • PubMed
    • Google Scholar
19. 1. Chandler DJ
    2. Lamperski CS
    3. Waterhouse BD

    (2013) Identification and distribution of projections from monoaminergic and cholinergic nuclei to functionally differentiated subregions of prefrontal cortex

    Brain Research 1522:38–58.

    https://doi.org/10.1016/j.brainres.2013.04.057

    • PubMed
    • Google Scholar
20. 1. Chang MH
    2. Armstrong KM
    3. Moore T

    (2012) Dissociation of response variability from firing rate effects in frontal eye field neurons during visual stimulation, working memory, and attention

    The Journal of Neuroscience 32:2204–2216.

    https://doi.org/10.1523/JNEUROSCI.2967-11.2012

    • PubMed
    • Google Scholar
21. 1. Churchland MM
    2. Yu BM
    3. Cunningham JP
    4. Sugrue LP
    5. Cohen MR
    6. Corrado GS
    7. Newsome WT
    8. Clark AM
    9. Hosseini P
    10. Scott BB
    11. Bradley DC
    12. Smith MA
    13. Kohn A
    14. Movshon JA
    15. Armstrong KM
    16. Moore T
    17. Chang SW
    18. Snyder LH
    19. Lisberger SG
    20. Priebe NJ
    21. Finn IM
    22. Ferster D
    23. Ryu SI
    24. Santhanam G
    25. Sahani M
    26. Shenoy KV

    (2010) Stimulus onset quenches neural variability: a widespread cortical phenomenon

    Nature Neuroscience 13:369–378.

    https://doi.org/10.1038/nn.2501

    • PubMed
    • Google Scholar
22. 1. Cohen MR
    2. Maunsell JHR

    (2009) Attention improves performance primarily by reducing interneuronal correlations

    Nature Neuroscience 12:1594–1600.

    https://doi.org/10.1038/nn.2439

    • PubMed
    • Google Scholar
23. 1. Cohen M.R.
    2. Kohn A

    (2011) Measuring and interpreting neuronal correlations

    Nature Neuroscience 14:811–819.

    https://doi.org/10.1038/nn.2842

    • PubMed
    • Google Scholar
24. 1. Critchley HD
    2. Tang J
    3. Glaser D
    4. Butterworth B
    5. Dolan RJ

    (2005) Anterior cingulate activity during error and autonomic response

    NeuroImage 27:885–895.

    https://doi.org/10.1016/j.neuroimage.2005.05.047

    • PubMed
    • Google Scholar
25. 1. Dalley JW
    2. McGaughy J
    3. O’Connell MT
    4. Cardinal RN
    5. Levita L
    6. Robbins TW

    (2001)

    Distinct changes in cortical acetylcholine and noradrenaline efflux during contingent and noncontingent performance of a visual attentional task

    The Journal of Neuroscience 21:4908–4914.

    • PubMed
    • Google Scholar
26. 1. De Gee JW
    2. Colizoli O
    3. Kloosterman NA
    4. Knapen T
    5. Nieuwenhuis S
    6. Donner TH

    (2017) Dynamic modulation of decision biases by brainstem arousal systems

    eLife 6:e23232.

    https://doi.org/10.7554/eLife.23232

    • Google Scholar
27. 1. de la Rocha J
    2. Doiron B
    3. Shea-Brown E
    4. Josić K
    5. Reyes A

    (2007) Correlation between neural spike trains increases with firing rate

    Nature 448:802–806.

    https://doi.org/10.1038/nature06028

    • PubMed
    • Google Scholar
28. 1. Devauges V
    2. Sara SJ

    (1990) Activation of the noradrenergic system facilitates an attentional shift in the rat

    Behavioural Brain Research 39:19–28.

    https://doi.org/10.1016/0166-4328(90)90118-x

    • PubMed
    • Google Scholar
29. 1. Devilbiss DM
    2. Waterhouse BD

    (2011) Phasic and tonic patterns of locus coeruleus output differentially modulate sensory network function in the awake rat

    Journal of Neurophysiology 105:69–87.

    https://doi.org/10.1152/jn.00445.2010

    • PubMed
    • Google Scholar
30. 1. Dillier N
    2. Laszlo J
    3. Müller B
    4. Koella WP
    5. Olpe HR

    (1978) Activation of an inhibitory noradrenergic pathway projecting from the locus coeruleus to the cingulate cortex of the rat

    Brain Research 154:61–68.

    https://doi.org/10.1016/0006-8993(78)91051-x

    • PubMed
    • Google Scholar
31. 1. Ding L
    2. Perkel DJ

    (2002) Dopamine modulates excitability of spiny neurons in the avian basal ganglia

    The Journal of Neuroscience 22:5210–5218.

    https://doi.org/10.1523/JNEUROSCI.22-12-05210.2002

    • PubMed
    • Google Scholar
32. 1. Doiron B
    2. Litwin-Kumar A
    3. Rosenbaum R
    4. Ocker GK
    5. Josić K

    (2016) The mechanics of state-dependent neural correlations

    Nature Neuroscience 19:383–393.

    https://doi.org/10.1038/nn.4242

    • PubMed
    • Google Scholar
33. 1. Downer JD
    2. Niwa M
    3. Sutter ML

    (2015) Task engagement selectively modulates neural correlations in primary auditory cortex

    The Journal of Neuroscience 35:7565–7574.

    https://doi.org/10.1523/JNEUROSCI.4094-14.2015

    • PubMed
    • Google Scholar
34. 1. Doyle WI
    2. Meeks JP

    (2017) Heterogeneous effects of norepinephrine on spontaneous and stimulus-driven activity in the male accessory olfactory bulb

    Journal of Neurophysiology 117:1342–1351.

    https://doi.org/10.1152/jn.00871.2016

    • PubMed
    • Google Scholar
35. 1. Ebitz RB
    2. Platt ML

    (2015) Neuronal activity in primate dorsal anterior cingulate cortex signals task conflict and predicts adjustments in pupil-linked arousal

    Neuron 85:628–640.

    https://doi.org/10.1016/j.neuron.2014.12.053

    • PubMed
    • Google Scholar
36. 1. Ecker AS
    2. Berens P
    3. Keliris GA
    4. Bethge M
    5. Logothetis NK
    6. Tolias AS

    (2010) Decorrelated neuronal firing in cortical microcircuits

    Science 327:584–587.

    https://doi.org/10.1126/science.1179867

    • PubMed
    • Google Scholar
37. 1. Ecker AS
    2. Denfield GH
    3. Bethge M
    4. Tolias AS

    (2016) On the Structure of Neuronal Population Activity under Fluctuations in Attentional State

    The Journal of Neuroscience 36:1775–1789.

    https://doi.org/10.1523/JNEUROSCI.2044-15.2016

    • PubMed
    • Google Scholar
38. 1. Einhäuser W
    2. Koch C
    3. Carter OL

    (2010) Pupil dilation betrays the timing of decisions

    Frontiers in Human Neuroscience 4:18.

    https://doi.org/10.3389/fnhum.2010.00018

    • PubMed
    • Google Scholar
39. 1. Falkner AL
    2. Goldberg ME
    3. Krishna BS

    (2013) Spatial representation and cognitive modulation of response variability in the lateral intraparietal area priority map

    The Journal of Neuroscience 33:16117–16130.

    https://doi.org/10.1523/JNEUROSCI.5269-12.2013

    • PubMed
    • Google Scholar
40. Book

    1. Feldman RS RS
    2. Meyer JS JS
    3. Quenzer LF

    (1997)

    In: Principles of Neuropsychopharmacology

    Sinauer.

    • Google Scholar
41. 1. Fernández-Pastor B
    2. Meana JJ

    (2002) In vivo tonic modulation of the noradrenaline release in the rat cortex by locus coeruleus somatodendritic alpha(2)-adrenoceptors

    European Journal of Pharmacology 442:225–229.

    https://doi.org/10.1016/s0014-2999(02)01543-1

    • PubMed
    • Google Scholar
42. 1. Fernández-Pastor B
    2. Mateo Y
    3. Gómez-Urquijo S
    4. Javier Meana J

    (2005) Characterization of noradrenaline release in the locus coeruleus of freely moving awake rats by in vivo microdialysis

    Psychopharmacology 180:570–579.

    https://doi.org/10.1007/s00213-005-2181-y

    • PubMed
    • Google Scholar
43. 1. Flores-Hernandez J
    2. Hernandez S
    3. Snyder GL
    4. Yan Z
    5. Fienberg AA
    6. Moss SJ
    7. Greengard P
    8. Surmeier DJ

    (2000) D(1) dopamine receptor activation reduces GABA(A) receptor currents in neostriatal neurons through a PKA/DARPP-32/PP1 signaling cascade

    Journal of Neurophysiology 83:2996–3004.

    https://doi.org/10.1152/jn.2000.83.5.2996

    • PubMed
    • Google Scholar
44. 1. Garcia-Junco-Clemente P
    2. Tring E
    3. Ringach DL
    4. Trachtenberg JT

    (2019) State-Dependent Subnetworks of Parvalbumin-Expressing Interneurons in Neocortex

    Cell Reports 26:2282–2288.

    https://doi.org/10.1016/j.celrep.2019.02.005

    • PubMed
    • Google Scholar
45. 1. Gehring WJ
    2. Willoughby AR

    (2002) The medial frontal cortex and the rapid processing of monetary gains and losses

    Science 295:2279–2282.

    https://doi.org/10.1126/science.1066893

    • PubMed
    • Google Scholar
46. 1. Gilzenrat MS
    2. Nieuwenhuis S
    3. Jepma M
    4. Cohen JD

    (2010) Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function

    Cognitive, Affective & Behavioral Neuroscience 10:252–269.

    https://doi.org/10.3758/CABN.10.2.252

    • PubMed
    • Google Scholar
47. 1. Gompf HS
    2. Mathai C
    3. Fuller PM
    4. Wood DA
    5. Pedersen NP
    6. Saper CB
    7. Lu J

    (2010) Locus ceruleus and anterior cingulate cortex sustain wakefulness in a novel environment

    The Journal of Neuroscience 30:14543–14551.

    https://doi.org/10.1523/JNEUROSCI.3037-10.2010

    • PubMed
    • Google Scholar
48. 1. Gray CM
    2. König P
    3. Engel AK
    4. Singer W

    (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties

    Nature 338:334–337.

    https://doi.org/10.1038/338334a0

    • PubMed
    • Google Scholar
49. 1. Gray CM

    (1999) The temporal correlation hypothesis of visual feature integration: still alive and well

    Neuron 24:31–47.

    https://doi.org/10.1016/s0896-6273(00)80820-x

    • PubMed
    • Google Scholar
50. 1. Guan D
    2. Armstrong WE
    3. Foehring RC

    (2015) dependence and differential modulation by norepinephrine

    Journal of Neurophysiology 113:2014–2032.

    https://doi.org/10.1152/jn.00524.2014

    • PubMed
    • Google Scholar
51. 1. Hayden BY
    2. Heilbronner SR
    3. Pearson JM
    4. Platt ML

    (2011) Surprise signals in anterior cingulate cortex: neuronal encoding of unsigned reward prediction errors driving adjustment in behavior

    The Journal of Neuroscience 31:4178–4187.

    https://doi.org/10.1523/JNEUROSCI.4652-10.2011

    • PubMed
    • Google Scholar
52. 1. Herrero JL
    2. Gieselmann MA
    3. Sanayei M
    4. Thiele A

    (2013) Attention-induced variance and noise correlation reduction in macaque V1 is mediated by NMDA receptors

    Neuron 78:729–739.

    https://doi.org/10.1016/j.neuron.2013.03.029

    • PubMed
    • Google Scholar
53. 1. Holroyd CB
    2. Coles MGH

    (2002) The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity

    Psychological Review 109:679–709.

    https://doi.org/10.1037/0033-295X.109.4.679

    • PubMed
    • Google Scholar
54. 1. Jones RS
    2. Olpe HR

    (1984) Activation of the noradrenergic projection from locus coeruleus reduces the excitatory responses of anterior cingulate cortical neurones to substance P

    Neuroscience 13:819–825.

    https://doi.org/10.1016/0306-4522(84)90098-8

    • PubMed
    • Google Scholar
55. 1. Joshi S
    2. Li Y
    3. Kalwani RM
    4. Gold JI

    (2016) Relationships between Pupil Diameter and Neuronal Activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex

    Neuron 89:221–234.

    https://doi.org/10.1016/j.neuron.2015.11.028

    • PubMed
    • Google Scholar
56. 1. Joshi S
    2. Gold JI

    (2020) Pupil Size as a Window on Neural Substrates of Cognition

    Trends in Cognitive Sciences 24:466–480.

    https://doi.org/10.1016/j.tics.2020.03.005

    • PubMed
    • Google Scholar
57. Software

    1. Joshi S
    2. Gold JI

    (2021) LC_ACC_paper_Joshi_Gold_2021, GitHub, version 22b8c94

    Github.

    https://github.com/TheGoldLab/LC_ACC_paper_Joshi_Gold_2021.git
58. 1. Kalwani RM
    2. Bloy L
    3. Elliott MA
    4. Gold JI

    (2009) A method for localizing microelectrode trajectories in the macaque brain using MRI

    Journal of Neuroscience Methods 176:104–111.

    https://doi.org/10.1016/j.jneumeth.2008.08.034

    • PubMed
    • Google Scholar
59. 1. Kalwani RM
    2. Joshi S
    3. Gold JI

    (2014) Phasic activation of individual neurons in the locus ceruleus/subceruleus complex of monkeys reflects rewarded decisions to go but not stop

    The Journal of Neuroscience 34:13656–13669.

    https://doi.org/10.1523/JNEUROSCI.2566-14.2014

    • PubMed
    • Google Scholar
60. 1. Kanitscheider I
    2. Coen-Cagli R
    3. Kohn A
    4. Pouget A

    (2015) Measuring Fisher information accurately in correlated neural populations

    PLOS Computational Biology 11:e1004218.

    https://doi.org/10.1371/journal.pcbi.1004218

    • PubMed
    • Google Scholar
61. 1. Khani A
    2. Rainer G

    (2016) Neural and neurochemical basis of reinforcement-guided decision making

    Journal of Neurophysiology 116:724–741.

    https://doi.org/10.1152/jn.01113.2015

    • PubMed
    • Google Scholar
62. 1. Koga K
    2. Yamada A
    3. Song Q
    4. Li XH
    5. Chen QY
    6. Liu RH
    7. Ge J
    8. Zhan C
    9. Furue H
    10. Zhuo M
    11. Chen T

    (2020) Ascending noradrenergic excitation from the locus coeruleus to the anterior cingulate cortex

    Molecular Brain 13:49.

    https://doi.org/10.1186/s13041-020-00586-5

    • PubMed
    • Google Scholar
63. 1. Köhler S
    2. Bär KJ
    3. Wagner G

    (2016) Differential involvement of brainstem noradrenergic and midbrain dopaminergic nuclei in cognitive control

    Human Brain Mapping 37:2305–2318.

    https://doi.org/10.1002/hbm.23173

    • PubMed
    • Google Scholar
64. 1. Kohn A
    2. Smith MA

    (2005) Stimulus dependence of neuronal correlation in primary visual cortex of the macaque

    The Journal of Neuroscience 25:3661–3673.

    https://doi.org/10.1523/JNEUROSCI.5106-04.2005

    • PubMed
    • Google Scholar
65. 1. Kohn A
    2. Coen-Cagli R
    3. Kanitscheider I
    4. Pouget A

    (2016) Correlations and Neuronal Population Information

    Annual Review of Neuroscience 39:237–256.

    https://doi.org/10.1146/annurev-neuro-070815-013851

    • PubMed
    • Google Scholar
66. 1. Kolta A
    2. Diop L
    3. Reader TA

    (1987) Noradrenergic effects on rat visual cortex: single-cell microiontophoretic studies of alpha-2 adrenergic receptors

    Life Sciences 41:281–289.

    https://doi.org/10.1016/0024-3205(87)90150-0

    • PubMed
    • Google Scholar
67. 1. Kupfermann I

    (1979) Modulatory actions of neurotransmitters

    Annual Review of Neuroscience 2:447–465.

    https://doi.org/10.1146/annurev.ne.02.030179.002311

    • PubMed
    • Google Scholar
68. 1. Lee V
    2. Pawlisch BA
    3. Macedo-Lima M
    4. Remage-Healey L

    (2018) Norepinephrine enhances song responsiveness and encoding in the auditory forebrain of male zebra finches

    Journal of Neurophysiology 119:209–220.

    https://doi.org/10.1152/jn.00251.2017

    • PubMed
    • Google Scholar
69. 1. Lewis MS
    2. Molliver ME
    3. Morrison JH
    4. Lidov HG

    (1979) Complmentarity of dopaminergic and noradrenergic innervation in anterior cingulate cortex of the rat

    Brain Research 164:328–333.

    https://doi.org/10.1016/0006-8993(79)90031-3

    • PubMed
    • Google Scholar
70. 1. Lin IC
    2. Okun M
    3. Carandini M
    4. Harris KD

    (2015) The Nature of Shared Cortical Variability

    Neuron 87:644–656.

    https://doi.org/10.1016/j.neuron.2015.06.035

    • PubMed
    • Google Scholar
71. 1. Lombardo JA
    2. Macellaio MV
    3. Liu B
    4. Palmer SE
    5. Osborne LC
    6. Doiron B

    (2018) State dependence of stimulus-induced variability tuning in macaque MT

    PLOS Computational Biology 14:e1006527.

    https://doi.org/10.1371/journal.pcbi.1006527

    • Google Scholar
72. Book

    1. Lowenstein O
    2. Loewenfeld IE

    (1969)

    The pupil

    In: Davson H, editors. The Eye. New York: Academic. pp. 255–337.

    • Google Scholar
73. 1. Matsumoto M
    2. Matsumoto K
    3. Abe H
    4. Tanaka K

    (2007) Medial prefrontal cell activity signaling prediction errors of action values

    Nature Neuroscience 10:647–656.

    https://doi.org/10.1038/nn1890

    • PubMed
    • Google Scholar
74. 1. McAdams CJ
    2. Maunsell JHR

    (1999) Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4

    The Journal of Neuroscience 19:431–441.

    https://doi.org/10.1523/JNEUROSCI.19-01-00431.1999

    • PubMed
    • Google Scholar
75. 1. McCormick DA
    2. Prince DA

    (1988) Noradrenergic modulation of firing pattern in guinea pig and cat thalamic neurons, in vitro

    Journal of Neurophysiology 59:978–996.

    https://doi.org/10.1152/jn.1988.59.3.978

    • PubMed
    • Google Scholar
76. 1. McGaughy J
    2. Ross RS
    3. Eichenbaum H

    (2008) Noradrenergic, but not cholinergic, deafferentation of prefrontal cortex impairs attentional set-shifting

    Neuroscience 153:63–71.

    https://doi.org/10.1016/j.neuroscience.2008.01.064

    • PubMed
    • Google Scholar
77. 1. McGinley MJ
    2. David SV
    3. McCormick DA

    (2015) Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection

    Neuron 87:179–192.

    https://doi.org/10.1016/j.neuron.2015.05.038

    • PubMed
    • Google Scholar
78. 1. McLean J
    2. Waterhouse BD

    (1994) Noradrenergic modulation of cat area 17 neuronal responses to moving visual stimuli

    Brain Research 667:83–97.

    https://doi.org/10.1016/0006-8993(94)91716-7

    • PubMed
    • Google Scholar
79. 1. Minces V
    2. Pinto L
    3. Dan Y
    4. Chiba AA

    (2017) Cholinergic shaping of neural correlations

    PNAS 114:5725–5730.

    https://doi.org/10.1073/pnas.1621493114

    • PubMed
    • Google Scholar
80. 1. Mitchell JF
    2. Sundberg KA
    3. Reynolds JH

    (2009) Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4

    Neuron 63:879–888.

    https://doi.org/10.1016/j.neuron.2009.09.013

    • PubMed
    • Google Scholar
81. 1. Moreno-Bote R
    2. Beck J
    3. Kanitscheider I
    4. Pitkow X
    5. Latham P
    6. Pouget A

    (2014) Information-limiting correlations

    Nature Neuroscience 17:1410–1417.

    https://doi.org/10.1038/nn.3807

    • PubMed
    • Google Scholar
82. 1. Morrison JH
    2. Molliver ME
    3. Grzanna R
    4. Coyle JT

    (1979) Noradrenergic innervation patterns in three regions of medial cortex: an immunofluorescence characterization

    Brain Research Bulletin 4:849–857.

    https://doi.org/10.1016/0361-9230(79)90022-4

    • PubMed
    • Google Scholar
83. 1. Murphy PR
    2. Robertson IH
    3. Balsters JH
    4. O’connell RG

    (2011) Pupillometry and P3 index the locus coeruleus-noradrenergic arousal function in humans

    Psychophysiology 48:1532–1543.

    https://doi.org/10.1111/j.1469-8986.2011.01226.x

    • PubMed
    • Google Scholar
84. 1. Murphy PR
    2. O’ RG
    3. Sullivan M
    4. Robertson IH
    5. Balsters JH

    (2014) Pupil diameter covaries with BOLD activity in human locus coeruleus

    Human Brain Mapping 35:4140–4154.

    https://doi.org/10.1002/hbm.22466

    • PubMed
    • Google Scholar
85. 1. Nicholas AP
    2. Pieribone V
    3. Hökfelt T

    (1993) Distributions of mRNAs for alpha-2 adrenergic receptor subtypes in rat brain: an in situ hybridization study

    The Journal of Comparative Neurology 328:575–594.

    https://doi.org/10.1002/cne.903280409

    • PubMed
    • Google Scholar
86. 1. Nieuwenhuis S
    2. De Geus EJ
    3. Aston-Jones G

    (2011) The anatomical and functional relationship between the P3 and autonomic components of the orienting response

    Psychophysiology 48:162–175.

    https://doi.org/10.1111/j.1469-8986.2010.01057.x

    • PubMed
    • Google Scholar
87. 1. Nirenberg S
    2. Latham PE

    (2003) Decoding neuronal spike trains: How important are correlations?

    PNAS 100:7348–7353.

    https://doi.org/10.1073/pnas.1131895100

    • Google Scholar
88. 1. Ohshima M
    2. Itami C
    3. Kimura F

    (2017) The α2A -adrenoceptor suppresses excitatory synaptic transmission to both excitatory and inhibitory neurons in layer 4 barrel cortex

    The Journal of Physiology 595:6923–6937.

    https://doi.org/10.1113/JP275142

    • PubMed
    • Google Scholar
89. 1. Parker AJ
    2. Newsome WT

    (1998) Sense and the single neuron: probing the physiology of perception

    Annual Review of Neuroscience 21:227–277.

    https://doi.org/10.1146/annurev.neuro.21.1.227

    • PubMed
    • Google Scholar
90. 1. Paukert M
    2. Agarwal A
    3. Cha J
    4. Doze VA
    5. Kang JU
    6. Bergles DE

    (2014) Norepinephrine controls astroglial responsiveness to local circuit activity

    Neuron 82:1263–1270.

    https://doi.org/10.1016/j.neuron.2014.04.038

    • PubMed
    • Google Scholar
91. Book

    1. Paxinos G
    2. Huang XF
    3. Petrides M
    4. Toga A

    (2008)

    The Rhesus Monkey Brain in Stereotaxic Coordinates

    San Diego, California, USA: Academic Press.

    • Google Scholar
92. 1. Pinto L
    2. Goard MJ
    3. Estandian D
    4. Xu M
    5. Kwan AC
    6. Lee SH
    7. Harrison TC
    8. Feng G
    9. Dan Y

    (2013) Fast modulation of visual perception by basal forebrain cholinergic neurons

    Nature Neuroscience 16:1857–1863.

    https://doi.org/10.1038/nn.3552

    • PubMed
    • Google Scholar
93. 1. Pong M
    2. Fuchs AF

    (2000) Characteristics of the pupillary light reflex in the macaque monkey: metrics

    Journal of Neurophysiology 84:953–963.

    https://doi.org/10.1152/jn.2000.84.2.953

    • PubMed
    • Google Scholar
94. 1. Porrino LJ
    2. Goldman-Rakic PS

    (1982) Brainstem innervation of prefrontal and anterior cingulate cortex in the rhesus monkey revealed by retrograde transport of HRP

    The Journal of Comparative Neurology 205:63–76.

    https://doi.org/10.1002/cne.902050107

    • PubMed
    • Google Scholar
95. 1. Purcell BA
    2. Heitz RP
    3. Cohen JY
    4. Schall JD

    (2012) Response variability of frontal eye field neurons modulates with sensory input and saccade preparation but not visual search salience

    Journal of Neurophysiology 108:2737–2750.

    https://doi.org/10.1152/jn.00613.2012

    • PubMed
    • Google Scholar
96. 1. Reimer J
    2. Froudarakis E
    3. Cadwell CR
    4. Yatsenko D
    5. Denfield GH
    6. Tolias AS

    (2014) Pupil fluctuations track fast switching of cortical states during quiet wakefulness

    Neuron 84:355–362.

    https://doi.org/10.1016/j.neuron.2014.09.033

    • PubMed
    • Google Scholar
97. 1. Reimer J
    2. McGinley MJ
    3. Liu Y
    4. Rodenkirch C
    5. Wang Q
    6. McCormick DA
    7. Tolias AS

    (2016) Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex

    Nature Communications 7:13289.

    https://doi.org/10.1038/ncomms13289

    • PubMed
    • Google Scholar
98. 1. Renart A
    2. de la Rocha J
    3. Bartho P
    4. Hollender L
    5. Parga N
    6. Reyes A
    7. Harris KD

    (2010) The asynchronous state in cortical circuits

    Science 327:587–590.

    https://doi.org/10.1126/science.1179850

    • PubMed
    • Google Scholar
99. 1. Riehle A
    2. Grün S
    3. Diesmann M
    4. Aertsen A

    (1997) Spike synchronization and rate modulation differentially involved in motor cortical function

    Science 278:1950–1953.

    https://doi.org/10.1126/science.278.5345.1950

    • PubMed
    • Google Scholar
100. 1. Robinson DA

     (1972) Eye movements evoked by collicular stimulation in the alert monkey

     Vision Research 12:1795–1808.

     https://doi.org/10.1016/0042-6989(72)90070-3

     • PubMed
     • Google Scholar
101. 1. Rodenkirch C
     2. Liu Y
     3. Schriver BJ
     4. Wang Q

     (2019) Locus coeruleus activation enhances thalamic feature selectivity via norepinephrine regulation of intrathalamic circuit dynamics

     Nature Neuroscience 22:120–133.

     https://doi.org/10.1038/s41593-018-0283-1

     • PubMed
     • Google Scholar
102. 1. Salamone JD
     2. Correa M
     3. Farrar AM
     4. Nunes EJ
     5. Pardo M

     (2009) Dopamine, behavioral economics, and effort

     Frontiers in Behavioral Neuroscience 3:13.

     https://doi.org/10.3389/neuro.08.013.2009

     • PubMed
     • Google Scholar
103. 1. Salgado H
     2. Garcia-Oscos F
     3. Patel A
     4. Martinolich L
     5. Nichols JA
     6. Dinh L
     7. Roychowdhury S
     8. Tseng KY
     9. Atzori M

     (2011) Layer-specific noradrenergic modulation of inhibition in cortical layer II/III

     Cerebral Cortex 21:212–221.

     https://doi.org/10.1093/cercor/bhq081

     • PubMed
     • Google Scholar
104. 1. Salgado H
     2. Treviño M
     3. Atzori M

     (2016) Layer- and area-specific actions of norepinephrine on cortical synaptic transmission

     Brain Research 1641:163–176.

     https://doi.org/10.1016/j.brainres.2016.01.033

     • PubMed
     • Google Scholar
105. 1. Sarafyazd M
     2. Jazayeri M

     (2019) Hierarchical reasoning by neural circuits in the frontal cortex

     Science 364:eaav8911.

     https://doi.org/10.1126/science.aav8911

     • PubMed
     • Google Scholar
106. 1. Schiemann J
     2. Puggioni P
     3. Dacre J
     4. Pelko M
     5. Domanski A
     6. van Rossum MCW
     7. Duguid I

     (2015) Cellular mechanisms underlying behavioral state-dependent bidirectional modulation of motor cortex output

     Cell Reports 11:1319–1330.

     https://doi.org/10.1016/j.celrep.2015.04.042

     • PubMed
     • Google Scholar
107. 1. Schmidt SL
     2. Chew EY
     3. Bennett DV
     4. Hammad MA
     5. Fröhlich F

     (2013) Differential effects of cholinergic and noradrenergic neuromodulation on spontaneous cortical network dynamics

     Neuropharmacology 72:259–273.

     https://doi.org/10.1016/j.neuropharm.2013.04.045

     • PubMed
     • Google Scholar
108. 1. Schultz W
     2. Apicella P
     3. Ljungberg T

     (1993) Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task

     The Journal of Neuroscience 13:900–913.

     https://doi.org/10.1523/JNEUROSCI.13-03-00900.1993

     • PubMed
     • Google Scholar
109. 1. Segal M
     2. Bloom FE

     (1976) The action of norepinephrine in the rat hippocampus. III. Hippocampal cellular responses to locus coeruleus stimulation in the awake rat

     Brain Research 107:499–511.

     https://doi.org/10.1016/0006-8993(76)90140-2

     • PubMed
     • Google Scholar
110. 1. Sharma Y
     2. Xu T
     3. Graf WM
     4. Fobbs A
     5. Sherwood CC
     6. Hof PR
     7. Allman JM
     8. Manaye KF

     (2010) Comparative anatomy of the locus coeruleus in humans and nonhuman primates

     The Journal of Comparative Neurology 518:963–971.

     https://doi.org/10.1002/cne.22249

     • PubMed
     • Google Scholar
111. 1. Shenhav A
     2. Botvinick MM
     3. Cohen JD

     (2013) The expected value of control: an integrative theory of anterior cingulate cortex function

     Neuron 79:217–240.

     https://doi.org/10.1016/j.neuron.2013.07.007

     • PubMed
     • Google Scholar
112. 1. Sherpa AD
     2. Xiao F
     3. Joseph N
     4. Aoki C
     5. Hrabetova S

     (2016) Activation of β-adrenergic receptors in rat visual cortex expands astrocytic processes and reduces extracellular space volume

     Synapse 70:307–316.

     https://doi.org/10.1002/syn.21908

     • PubMed
     • Google Scholar
113. 1. Shima K
     2. Tanji J

     (1998) Role for cingulate motor area cells in voluntary movement selection based on reward

     Science 282:1335–1338.

     https://doi.org/10.1126/science.282.5392.1335

     • PubMed
     • Google Scholar
114. 1. Silver RA

     (2010) Neuronal arithmetic

     Nature Reviews. Neuroscience 11:474–489.

     https://doi.org/10.1038/nrn2864

     • PubMed
     • Google Scholar
115. 1. Singer W
     2. Gray CM

     (1995) Visual feature integration and the temporal correlation hypothesis

     Annual Review of Neuroscience 18:555–586.

     https://doi.org/10.1146/annurev.ne.18.030195.003011

     • PubMed
     • Google Scholar
116. 1. Singer W

     (1999) Neuronal synchrony: a versatile code for the definition of relations?

     Neuron 24:49–65.

     https://doi.org/10.1016/s0896-6273(00)80821-1

     • PubMed
     • Google Scholar
117. 1. Smith MA
     2. Kohn A

     (2008) Spatial and temporal scales of neuronal correlation in primary visual cortex

     The Journal of Neuroscience 28:12591–12603.

     https://doi.org/10.1523/JNEUROSCI.2929-08.2008

     • PubMed
     • Google Scholar
118. 1. Sparks DL
     2. Nelson IS

     (1987) Sensory and motor maps in the mammalian superior colliculus

     Trends in Neurosciences 10:312–317.

     https://doi.org/10.1016/0166-2236(87)90085-3

     • Google Scholar
119. 1. Sugihara H
     2. Chen N
     3. Sur M

     (2016) Cell-specific modulation of plasticity and cortical state by cholinergic inputs to the visual cortex

     Journal of Physiology, Paris 110:37–43.

     https://doi.org/10.1016/j.jphysparis.2016.11.004

     • PubMed
     • Google Scholar
120. 1. Tervo DGR
     2. Proskurin M
     3. Manakov M
     4. Kabra M
     5. Vollmer A
     6. Branson K
     7. Karpova AY

     (2014) Behavioral variability through stochastic choice and its gating by anterior cingulate cortex

     Cell 159:21–32.

     https://doi.org/10.1016/j.cell.2014.08.037

     • PubMed
     • Google Scholar
121. 1. Thiele A
     2. Brandt C
     3. Dasilva M
     4. Gotthardt S
     5. Chicharro D
     6. Panzeri S
     7. Distler C

     (2016) Attention Induced Gain Stabilization in Broad and Narrow-Spiking Cells in the Frontal Eye-Field of Macaque Monkeys

     The Journal of Neuroscience 36:7601–7612.

     https://doi.org/10.1523/JNEUROSCI.0872-16.2016

     • PubMed
     • Google Scholar
122. 1. Timmons SD
     2. Geisert E
     3. Stewart AE
     4. Lorenzon NM
     5. Foehring RC

     (2004) alpha2-Adrenergic receptor-mediated modulation of calcium current in neocortical pyramidal neurons

     Brain Research 1014:184–196.

     https://doi.org/10.1016/j.brainres.2004.04.025

     • PubMed
     • Google Scholar
123. 1. Valente M
     2. Pica G
     3. Bondanelli G
     4. Moroni M
     5. Runyan CA
     6. Morcos AS
     7. Harvey CD
     8. Panzeri S

     (2021) Correlations enhance the behavioral readout of neural population activity in association cortex

     Nature Neuroscience 24:975–986.

     https://doi.org/10.1038/s41593-021-00845-1

     • PubMed
     • Google Scholar
124. 1. Varazzani C
     2. San-Galli A
     3. Gilardeau S
     4. Bouret S

     (2015) Noradrenaline and dopamine neurons in the reward/effort trade-off: a direct electrophysiological comparison in behaving monkeys

     The Journal of Neuroscience 35:7866–7877.

     https://doi.org/10.1523/JNEUROSCI.0454-15.2015

     • PubMed
     • Google Scholar
125. 1. Vinck M
     2. Batista-Brito R
     3. Knoblich U
     4. Cardin JA

     (2015) Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding

     Neuron 86:740–754.

     https://doi.org/10.1016/j.neuron.2015.03.028

     • PubMed
     • Google Scholar
126. 1. Wang Z
     2. McCormick DA

     (1993) Control of firing mode of corticotectal and corticopontine layer V burst-generating neurons by norepinephrine, acetylcholine, and 1S,3R-ACPD

     The Journal of Neuroscience 13:2199–2216.

     https://doi.org/10.1523/JNEUROSCI.13-05-02199.1993

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

     (1980) Noradrenergic modulation of somatosensory cortical neuronal responses to iontophoretically applied putative neurotransmitters

     Experimental Neurology 69:30–49.

     https://doi.org/10.1016/0014-4886(80)90141-7

     • PubMed
     • Google Scholar
128. 1. Xiang Z
     2. Huguenard JR
     3. Prince DA

     (1998) Cholinergic switching within neocortical inhibitory networks

     Science 281:985–988.

     https://doi.org/10.1126/science.281.5379.985

     • PubMed
     • Google Scholar
129. 1. Zohary E
     2. Shadlen MN
     3. Newsome WT

     (1994) Correlated neuronal discharge rate and its implications for psychophysical performance

     Nature 370:140–143.

     https://doi.org/10.1038/370140a0

     • PubMed
     • Google Scholar

Author details

1. Siddhartha Joshi

   Department of Neuroscience, University of Pennsylvania, Philadelphia, United States

   Contribution

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

   For correspondence

   thesidjoshi@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0529-9430

2. Joshua I Gold

   Department of Neuroscience, University of Pennsylvania, Philadelphia, United States

   Contribution

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

   Competing interests

   Senior editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-6018-0483

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