Neuromodulation of Attention
Authors: Alexander Thiele, Mark A. Bellgrove
Journal: Neuron (2018)

Attention is critical to high-level cognition and attention deficits are a hallmark of neurologic and neuropsychiatric disorders. Although years of research indicates that distinct neuromodulators influence attentional control, a mechanistic account that traverses levels of analysis (cells, circuits, behavior) is missing. However, such an account is critical to guide the development of next-generation pharmacotherapies aimed at forestalling or remediating the global burden associated with disorders of attention. Here, we summarize current neuroscientific understanding of how attention affects single neurons and networks of neurons. We then review key results that have informed our understanding of how neuromodulation shapes these neuron and network properties and thereby enables the appropriate allocation of attention to relevant external or internal events. Finally, we highlight areas where we believe hypotheses can be formulated and tackled experimentally in the near future, thereby critically increasing our mechanistic understanding of how attention is implemented at the cellular and network levels. Attention is critical to high-level cognition and attention deficits are a hallmark of neurologic and neuropsychiatric disorders. Although years of research indicates that distinct neuromodulators influence attentional control, a mechanistic account that traverses levels of analysis (cells, circuits, behavior) is missing. However, such an account is critical to guide the development of next-generation pharmacotherapies aimed at forestalling or remediating the global burden associated with disorders of attention. Here, we summarize current neuroscientific understanding of how attention affects single neurons and networks of neurons. We then review key results that have informed our understanding of how neuromodulation shapes these neuron and network properties and thereby enables the appropriate allocation of attention to relevant external or internal events. Finally, we highlight areas where we believe hypotheses can be formulated and tackled experimentally in the near future, thereby critically increasing our mechanistic understanding of how attention is implemented at the cellular and network levels. The limited processing capacity of the perceptual system poses a complex computational problem for humans and other organisms: which inputs are relevant to current behavioral goals? Decades of research has now been devoted to understanding how neurons instantiate the required "selectivity" that allows an organism to prioritize, or bias, the processing of relevant over irrelevant inputs. Neuronal processing may be biased by both bottom-up and top-down influences. The former reflects the biasing of sensory processing due to stimulus saliency (brightness, movement, size, for example), which causes features to "pop-out" from their surroundings to capture attention. Top-down processing on the other hand reflects the voluntary guidance of attention to locations, features, or objects in the environment. In this way, top-down attention allows for the voluntary processing of relevant over irrelevant inputs in line with the current behavioral goals of the organism (Desimone and Duncan, 1995Desimone R. Duncan J. Neural mechanisms of selective visual attention.Annu. Rev. Neurosci. 1995; 18: 193-222Crossref PubMed Google Scholar). A network of prefrontal and parietal cortical areas is critically involved in the selection required for top-down attention, and other high-level cognitive functions such as working memory or inhibitory control (Bichot et al., 2015Bichot N.P. Heard M.T. DeGennaro E.M. Desimone R. 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Neuromodulators configure neuronal circuits and thereby specify output properties (Marder, 2012Marder E. Neuromodulation of neuronal circuits: back to the future.Neuron. 2012; 76: 1-11Abstract Full Text Full Text PDF PubMed Scopus (324) Google Scholar). They thus shape information processing in local and large-scale neuronal networks, such that relevant, over irrelevant, information is prioritized. This in turn drives behavior the subject hopes to be rewarding or behavior that minimizes adverse outcomes. Understanding precisely how this is achieved at the level of single neurons, local networks, or large-scale networks is vital for basic and clinical neuroscience. Neuromodulators most strongly implicated in high-level cognitive functions are acetylcholine (ACh), dopamine (DA), noradrenaline (NA), and serotonin (5-HT). In this review, we focus on their relevance for top-down attentional control. While we will focus on their role in relation to attention, the above neuromodulators have major roles in other aspects of cognition, such as reward signaling (e.g., ACh and DA, Richardson and DeLong, 1986Richardson R.T. DeLong M.R. Nucleus basalis of Meynert neuronal activity during a delayed response task in monkey.Brain Res. 1986; 399: 364-368Crossref PubMed Google Scholar, Rutledge et al., 2015Rutledge R.B. Skandali N. Dayan P. Dolan R.J. Dopaminergic modulation of decision making and subjective well-being.J. Neurosci. 2015; 35: 9811-9822Crossref PubMed Scopus (66) Google Scholar, Schultz, 2015Schultz W. Neuronal reward and decision signals: from theories to data.Physiol. Rev. 2015; 95: 853-951Crossref PubMed Scopus (307) Google Scholar), working memory (DA, NA, Arnsten and Goldman-Rakic, 1990Arnsten A.F. Goldman-Rakic P.S. Analysis of alpha-2 adrenergic agonist effects on the delayed nonmatch-to-sample performance of aged rhesus monkeys.Neurobiol. 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An important point to address from the outset is how attention, i.e., the top-down prioritization of behaviorally relevant inputs, differs from working memory. Working memory can be conceived as an active process whereby stimulus or internal representations are stored "on-line" to prevent temporal decay or intrusion from competing or distracting stimuli that are outside the current focus of attention. Dissociating effects of attention from those of working memory is difficult and in practice the two processes are highly interactive (Awh and Jonides, 2001Awh E. Jonides J. Overlapping mechanisms of attention and spatial working memory.Trends Cogn. Sci. 2001; 5: 119-126Abstract Full Text Full Text PDF PubMed Scopus (770) Google Scholar). Attention, as conceptualized in this paper, is a selection mechanism that allows for the preferential processing of task-relevant information over irrelevant (distracting) information, i.e., it is a filter mechanism. This selection is driven by currently active behavioral goals held in working memory. In that sense, attention acts in the service of working memory. However, behavioral paradigms employed in neurophysiological studies highlight the potentially close coupling of attention and working memory (e.g., cue-guided spatial working memory tasks or cue-guided spatial attention task). In the cue-guided spatial working memory task, a brief spatial cue will capture attention, which then triggers the working memory signal that enacts the behavioral goal of a memory-guided saccade to cued spatial location. We contend that covert spatial attention would also be allocated to the cued location during the memory period, rendering the two processes largely inseparable. Here, the neural correlates of either process would be "delay activity"; that is, activity that occurs (or persists) even after a behaviorally relevant stimulus (or cue) is removed from the sensory environment (Fuster, 1973Fuster J.M. 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Such a low-level mechanistic account is necessary to validate work that conceptualizes high-level neuromodulator functions from a computational and theoretical perspective (for example, Yu and Dayan, 2005Yu A.J. Dayan P. Uncertainty, neuromodulation, and attention.Neuron. 2005; 46: 681-692Abstract Full Text Full Text PDF PubMed Scopus (828) Google Scholar), and it may help to understand why neuropharmacological manipulations can be task and context specific. Finally, such a mechanistic account is required to guide the development of next-generation pharmacotherapies. We note from the outset that, although a full understanding of the neuromodulation of attention is not possible given current data, we review the state-of-the-art and highlight important questions that can be addressed in the near future. Neuromodulators act mostly through metabotropic receptors, which activate different G-proteins and thereby trigger second messenger cascades. They affect different receptor types and can thereby enhance or reduce transmitter release, synaptic efficacy, or postsynaptic excitability in neuronal circuits. Exceptions to the metabotropic neuromodulator pathways exist for ACh, which can act through ionotropic nicotinic receptors, and for 5-HT, which can act through the ionotropic 5-HT3 receptor. Detailed reviews regarding the different receptors involved and their action can be found elsewhere (e.g., Beaulieu and Gainetdinov, 2011Beaulieu J.M. Gainetdinov R.R. The physiology, signaling, and pharmacology of dopamine receptors.Pharmacol. Rev. 2011; 63: 182-217Crossref PubMed Scopus (1273) Google Scholar, Hein, 2006Hein L. Adrenoceptors and signal transduction in neurons.Cell Tissue Res. 2006; 326: 541-551Crossref PubMed Scopus (126) Google Scholar, Nichols and Nichols, 2008Nichols D.E. Nichols C.D. Serotonin receptors.Chem. Rev. 2008; 108: 1614-1641Crossref PubMed Scopus (501) Google Scholar, Thiele, 2013Thiele A. 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To link neuromodulatory action to attentional effects on neuronal activity, we first provide a brief account of how top-down attention affects neuronal activity, i.e., attention that is directed to spatial locations, objects, or features of the world. Detailed review articles of these effects can be found elsewhere (e.g., Desimone and Duncan, 1995Desimone R. Duncan J. Neural mechanisms of selective visual attention.Annu. Rev. Neurosci. 1995; 18: 193-222Crossref PubMed Google Scholar, Maunsell and Treue, 2006Maunsell J.H. Treue S. Feature-based attention in visual cortex.Trends Neurosci. 2006; 29: 317-322Abstract Full Text Full Text PDF PubMed Scopus (517) Google Scholar, Maunsell, 2015Maunsell J.H.R. Neuronal mechanisms of visual attention.Annu. Rev. Vis. Sci. 2015; 1: 373-391Crossref PubMed Google Scholar, Miller and Buschman, 2013Miller E.K. Buschman T.J. Cortical circuits for the control of attention.Curr. Opin. Neurobiol. 2013; 23: 216-222Crossref PubMed Scopus (122) Google Scholar). Our aim here is to link the effects of attention on single neurons and circuits to those same effects reported for neuromodulators. This link is in many cases indirect, as our knowledge of the actions of neuromodulators on cell and population activity arises largely from studies where attention was not manipulated. Nevertheless, the extant data allow us to delineate likely scenarios that describe how neuromodulator actions could aid attentional selection. Over the course of the past 30 years, a large number of studies have delineated the neuronal signatures of top-down attention. Top-down attention is defined as the cognitive process by which an individual selects and prioritizes the processing of specific aspects of information. This cognitive process has neural correlates, and we use the term "top-down attention" as a descriptor of neuronal activity changes that occur following the selection process. The behavioral benefits associated with top-down attentional selection are then an emergent property of activity changes in single neurons and in neuronal populations that occur within an area and between multiple areas. One of the main neuronal correlates of attention is an alteration to neuronal firing rates (Reynolds et al., 2000Reynolds J.H. Pasternak T. Desimone R. Attention increases sensitivity of V4 neurons.Neuron. 2000; 26: 703-714Abstract Full Text Full Text PDF PubMed Google Scholar, Spitzer et al., 1988Spitzer H. Desimone R. Moran J. Increased attention enhances both behavioral and neuronal performance.Science. 1988; 240: 338-340Crossref PubMed Google Scholar, Treue and Maunsell, 1996Treue S. Maunsell J.H.R. Attentional modulation of visual motion processing in cortical areas MT and MST.Nature. 1996; 382: 539-541Crossref PubMed Scopus (719) Google Scholar). Neurons that represent the focus of attention respond differently to the inputs they receive. In general, neurons representing attended locations, features, or objects show increased firing rates. This phenomenon has been observed across all cortical and subcortical areas investigated (e.g., Krauzlis et al., 2013Krauzlis R.J. Lovejoy L.P. Zénon A. Superior colliculus and visual spatial attention.Annu. Rev. Neurosci. 2013; 36: 165-182Crossref PubMed Scopus (245) Google Scholar, Noudoost et al., 2010Noudoost B. Chang M.H. Steinmetz N.A. Moore T. Top-down control of visual attention.Curr. Opin. Neurobiol. 2010; 20: 183-190Crossref PubMed Scopus (174) Google Scholar). However, attention can also result in suppression of neuronal activity for unattended locations or features (Martinez-Trujillo and Treue, 2004Martinez-Trujillo J.C. Treue S. Feature-based attention increases the selectivity of population responses in primate visual cortex.Curr. Biol. 2004; 14: 744-751Abstract Full Text Full Text PDF PubMed Scopus (430) Google Scholar). The ways in which attention changes neuronal input-output relationships were originally captured by different models of "gain change." Gain change describes how neuronal input-output relationships are affected by attention. These gain changes can take different forms, such that responses are either proportionally increased for all stimuli (response gain change), or mostly for less-salient stimuli (contrast gain change), or by a reduction in responses to less-salient (non-preferred) stimuli and an increase in responses to salient (preferred) stimuli (see Figure 1A, Martinez-Trujillo and Treue, 2004Martinez-Trujillo J.C. Treue S. Feature-based attention increases the selectivity of population responses in primate visual cortex.Curr. Biol. 2004; 14: 744-751Abstract Full Text Full Text PDF PubMed Scopus (430) Google Scholar, McAdams and Maunsell, 1999McAdams C.J. Maunsell J.H.R. Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4.J. 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In extrastriate and frontal cortex, attention does indeed increase the activity in putative excitatory and putative inhibitory cells (Mitchell et al., 2007Mitchell J.F. Sundberg K.A. Reynolds J.H. Differential attention-dependent response modulation across cell classes in macaque visual area V4.Neuron. 2007; 55: 131-141Abstract Full Text Full Text PDF PubMed Scopus (397) Google Scholar, Thiele et al., 2016Thiele A. Brandt C. Dasilva M. Gotthardt S. Chicharro D. Panzeri S. Distler C. Attention induced gain stabilization in broad and narrow-spiking cells in the frontal eye-field of macaque monkeys.J. Neurosci. 2016; 36: 7601-7612Crossref PubMed Scopus (13) Google Scholar). At first glance, it may seem counterintuitive that attention increases firing rates, when increased inhibition should counter such an effect. However, this effect can be understood in terms of competing neuronal populations that represent different locations of features (Reynolds et al., 1999Reynolds J.H. Chelazzi L. Desimone R. Competitive mechanisms subserve attention in macaque areas V2 and V4.J. Neurosci. 1999; 19: 1736-1753Crossref PubMed Google Scholar). Attention alters the balance between excitation and inhibition of this competition (Reynolds and Heeger, 2009Reynolds J.H. Heeger D.J. The normalization model of attention.Neuron. 2009; 61: 168-185Abstract Full Text Full Text PDF PubMed Scopus (692) Google Scholar). The attention-induced normalization supports neuronal computations that enable winner-take-all states (Carandini and Heeger, 2011Carandini M. Heeger D.J. Normalization as a canonical neural computation.Nat. Rev. Neurosci. 2011; 13: 51-62Crossref PubMed Scopus (775) Google Scholar). Winner-take-all network states occur, for example, when attention biases one neuronal population over another to win the competition for sensory representation (Reynolds et al., 1999Reynolds J.H. Chelazzi L. Desimone R. Competitive mechanisms subserve attention in macaque areas V2 and V4.J. Neurosci. 1999; 19: 1736-1753Crossref PubMed Google Scholar, Wang, 2008Wang X.J. Decision making in recurrent neuronal circuits.Neuron. 2008; 60: 215-234Abstract Full Text Full Text PDF PubMed Scopus (330) Google Scholar). The winning population shows increased neuronal activity and suppresses its competitors. Competition can also occur in the decision space. When multiple decisions are possible but competing, the interaction between the representations converges toward a single (winning) choice (Reynolds et al., 1999Reynolds J.H. Chelazzi L. Desimone R. Competitive mechanisms subserve attention in macaque areas V2 and V4.J. Neurosci. 1999; 19: 1736-1753Crossref PubMed Google Scholar, Wang, 2008Wang X.J. Decision making in recurrent neuronal circuits.Neuron. 2008; 60: 215-234Abstract Full Text Full Text PDF PubMed Scopus (330) Google Scholar). These winner-take-all states occur naturally in neuronal attractor networks. Attractor networks move from labile states, where they can undergo regular and easy state transitions, to stable (attractor) states, where one representation dominates. The stability of these states is determined by the strength of inhibitory and excitatory drive (Deco and Hugues, 2012Deco G. Hugues E. Neural network mechanisms underlying stimulus driven variability reduction.PLoS Comput. Biol. 2012; 8: e1002395Crossref PubMed Scopus (55) Google Scholar, Deco et al., 2014Deco G. Ponce-Alvarez A. Hagmann P. Romani G.L. Mantini D. Corbetta M. How local excitation-inhibition ratio impacts the whole brain dynamics.J. Neurosci. 2014; 34: 7886-7898Crossref PubMed Scopus (86) Google Scholar). By increasing inhibitory and excitatory drive, attention can allow competing unstable patterns of neuronal activity (representations) to converge to a more stable pattern of activity, such that a single representation dominates (see Figure 1D for a schematic). These stable activity patterns can be associated with a specific attentional state (such as a location), an item held in working memory, a decision that has been formed, or a motor plan. Importantly, the stability of the activity pattern in the attractor network (illustrated by the depth of the valley in Figure 1D) can be influenced by different neuromodulators (see later parts of the review). Cognitive operations or neuromodulators supporting labile states would enhance cognitive flexibility (but potentially at the cost of increased distractibility), whereas cognitive operations or neuromodulators supporting stable states would benefit task focus. Stabilized attractor networks are characterized by increased firing rates for the winning population and decreased firing rates for the losing populations (the described gain changes). In addition, stabilized attractor networks exhibit reduced neuronal rate variability and rate co-variability (Deco and Hugues, 2012Deco G. Hugues E. 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