CoChat
Sign Up
Cited by 350Neural dynamics and brain functionNeural and Behavioral Psychology StudiesNeuroscience and Neuropharmacology Research

The DOI matches its Crossref record; 215 of the 215 references it cites were found in a registry or an index.

Neuromodulation of Attention

Open access
Thiele, Alexander, Bellgrove, Mark A.·2018·Neuron
DOI 10.1016/j.neuron.2018.01.008PMC PMC6204752
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neuron.2018.01.008

Cites

215 of 215 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Schematic that Exemplifies Some of the Effects Attention Has on Cellular Activity and on Population Activity Feedback and neuromodulator influences alter the drive in excitatory (blue) and in inhibitory (red) cells, which thereby exert increased influence on one another and the rest of the network, leading to overall increased, but balanced excitation, and inhibition. Exemplified are three scenarios, which have been described in the literature when attention is deployed to a spatial location and/or to specific stimulus features. These can also occur when neuromodulators are applied to the local network. (A) A result thereof is a gain change, i.e., a change of the output a neuron produces given a specific input. (B) Another effect of increased balanced excitation and inhibition is an increase in gamma oscillations of the local field potential, indicative of higher neuronal coherence, proposed to improve information exchange between selected neuronal populations. (C) Attention and neuromodulators also change the relationship between tuning similarity and noise correlations of neuron pairs, such that population coding properties are improved. (D) These changes are reminiscent of altered network attractor dynamics, which stabilize network states, reduce fluctuations, and increase the ability to stay task focused. a.u., arbitrary units; Hz, frequency in Hertz.
Figure 1 Schematic that Exemplifies Some of the Effects Attention Has on Cellular Activity and on Population Activity Feedback and neuromodulator influences alter the drive in excitatory (blue) and in inhibitory (red) cells, which thereby exert increased influence on one another and the rest of the network, leading to overall increased, but balanced excitation, and inhibition. Exemplified are three scenarios, which have been described in the literature when attention is deployed to a spatial location and/or to specific stimulus features. These can also occur when neuromodulators are applied to the local network. (A) A result thereof is a gain change, i.e., a change of the output a neuron produces given a specific input. (B) Another effect of increased balanced excitation and inhibition is an increase in gamma oscillations of the local field potential, indicative of higher neuronal coherence, proposed to improve information exchange between selected neuronal populations. (C) Attention and neuromodulators also change the relationship between tuning similarity and noise correlations of neuron pairs, such that population coding properties are improved. (D) These changes are reminiscent of altered network attractor dynamics, which stabilize network states, reduce fluctuations, and increase the ability to stay task focused. a.u., arbitrary units; Hz, frequency in Hertz.CC BY · PubMed Central
Figure 2 Influence of Tonic and Phasic Modes of Cholinergic Signaling on Attention, and Spatial Specificity of Cholinergic Input-Output Relations (A) Tonic levels of ACh in rodent prefrontal cortex are interspersed by brief phasic increases in ACh, which occur after behaviorally relevant cues, but only if they occur after “non-cue” trials. These are preceded by glutamate increases, which occur on all “cue detect” trials (after Sarter et al., 2014). (B) Potential source of spatial and temporal specificity of ACh signals in the cortex. ACh release in rodent prefrontal cortex is partly dependent on local glutamate activating presynaptic NMDA receptors. Local glutamate control of ACh in rat prefrontal cortex is released from mediodorsal thalamic inputs. Whether other glutamate sources (e.g., feedback from higher cortical areas) equally control ACh release locally is unknown. (C) Input to and output from cholinergic basal forebrain neuron is segregated into specific sub-circuits. Cholinergic BF neurons that project to the prefrontal cortex receive input mostly from neurons in the lateral septum, and from small, but segregated populations of the central amygdala. Cholinergic BF neurons that project to the motor cortex receive inputs from neurons in the somatosensory cortex, and segregated populations of neurons in the central amygdala and the caudate nucleus. Cholinergic BF neurons that project to the basolateral amygdala receive input from segregated populations in the central amygdala and the caudate nucleus (after Gielow and Zaborszky, 2017).
Figure 2 Influence of Tonic and Phasic Modes of Cholinergic Signaling on Attention, and Spatial Specificity of Cholinergic Input-Output Relations (A) Tonic levels of ACh in rodent prefrontal cortex are interspersed by brief phasic increases in ACh, which occur after behaviorally relevant cues, but only if they occur after “non-cue” trials. These are preceded by glutamate increases, which occur on all “cue detect” trials (after Sarter et al., 2014). (B) Potential source of spatial and temporal specificity of ACh signals in the cortex. ACh release in rodent prefrontal cortex is partly dependent on local glutamate activating presynaptic NMDA receptors. Local glutamate control of ACh in rat prefrontal cortex is released from mediodorsal thalamic inputs. Whether other glutamate sources (e.g., feedback from higher cortical areas) equally control ACh release locally is unknown. (C) Input to and output from cholinergic basal forebrain neuron is segregated into specific sub-circuits. Cholinergic BF neurons that project to the prefrontal cortex receive input mostly from neurons in the lateral septum, and from small, but segregated populations of the central amygdala. Cholinergic BF neurons that project to the motor cortex receive inputs from neurons in the somatosensory cortex, and segregated populations of neurons in the central amygdala and the caudate nucleus. Cholinergic BF neurons that project to the basolateral amygdala receive input from segregated populations in the central amygdala and the caudate nucleus (after Gielow and Zaborszky, 2017).CC BY · PubMed Central
Figure 3 Neuromodulation of WM Fields, Remote Feature Tuning, and Specificity of Dopaminergic Output Signals (A) Spatial tuning of WM fields is enhanced when small amounts of D1 agonists are applied in the vicinity of the neurons, by selectively reducing activity for non-preferred locations (memory fields). This is equivalent to a noise reduction. Spatial tuning of WM fields is equally enhanced when small amounts of NA α2A agonists are applied in the vicinity of the neurons, by selectively increasing activity for preferred locations (memory fields). This is equivalent to signal enhancement. Both changes increase the SNR. (B) Application of D1 antagonists to area FEF enhances the tuning of area V4 neurons that have overlapping receptive fields with the affected FEF locations. (C) Hypothetical interactions of dopaminergic subpopulations carrying specific; based on projections found in rat. dlPFC, dorsolateral prefrontal cortex; FEF, frontal eye field; VTA, ventral tegmental area; SN, substantia nigra; a.u., arbitrary units.
Figure 3 Neuromodulation of WM Fields, Remote Feature Tuning, and Specificity of Dopaminergic Output Signals (A) Spatial tuning of WM fields is enhanced when small amounts of D1 agonists are applied in the vicinity of the neurons, by selectively reducing activity for non-preferred locations (memory fields). This is equivalent to a noise reduction. Spatial tuning of WM fields is equally enhanced when small amounts of NA α2A agonists are applied in the vicinity of the neurons, by selectively increasing activity for preferred locations (memory fields). This is equivalent to signal enhancement. Both changes increase the SNR. (B) Application of D1 antagonists to area FEF enhances the tuning of area V4 neurons that have overlapping receptive fields with the affected FEF locations. (C) Hypothetical interactions of dopaminergic subpopulations carrying specific; based on projections found in rat. dlPFC, dorsolateral prefrontal cortex; FEF, frontal eye field; VTA, ventral tegmental area; SN, substantia nigra; a.u., arbitrary units.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

Sorted by citations

See all 350

Related

See all

Metadata

Authors
Thiele, A., & Bellgrove, M. A.
Affiliations
  • Newcastle University (GB)
  • Monash University (AU)
Corresponding author
Thiele, Alexander
ORCID
Venue
Neuron
Publisher
Elsevier BV
Published
2018-02
Volume
97
Issue
4
Pages
769-785
ISSN
0896-6273 (ISSN-L)
DOI
10.1016/j.neuron.2018.01.008
PMC
PMC6204752
OpenAlex
W2788231085
Cited by
350
References
215
Citations by year
2026: 36 · 2025: 33 · 2024: 54 · 2023: 52 · 2022: 45 · 2021: 50 · 2020: 32 · 2019: 35 · 2018: 13
FWCI
4.34
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuron (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Monash University Research Portal (Monash University) (submittedVersion, cc-by)
  • Newcastle University ePrints (Newcastle Univesity) (cc-by)
Funders
  • Wellcome Trust (093104)
  • MRC (MR/P013031/1)
  • NC3R (NC/P000940/1)
  • Australian Research Council (FT130101488, DP150100986)
  • Newcastle University
  • Monash University
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Keywords
attention, neuromodulation, attention deficits, single neurons, neural circuits, attentional control, attention allocation
MeSH terms
Acetylcholine, Animals, Attention, Biogenic Amines, Brain, Dopamine, Humans, Neural Pathways, Neurons, Norepinephrine, Serotonin

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review