Cited by 932Neural dynamics and brain functionSleep and Wakefulness ResearchVisual perception and processing mechanisms

The DOI matches its Crossref record; 173 of the 176 references it cites were found in a registry or an index; none of the 299 sources its claims cite is checked for support yet.

Waking State: Rapid Variations Modulate Neural and Behavioral Responses

Open access

Abstract

The state of the brain and body constantly varies on rapid and slow time scales. These variations contribute to the apparent noisiness of sensory responses at both the neural and behavioral level. Recent investigations of rapid state changes in awake, behaving animals have provided insight into the mechanisms by which optimal sensory encoding and behavioral performance are achieved. Fluctuations in state, as indexed by pupillometry, impact both the “signal” (sensory evoked response) and the “noise” (spontaneous activity) of cortical responses. By taking these fluctuations into account, neural response (co-)variability is significantly reduced, revealing the brain to be more reliable and predictable than previously thought.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review argues that waking brain state changes rapidly and continuously, and that pupil diameter plus movement can index these substates. Accounting for such fluctuations reduces apparent neural variability and shows that sensory encoding and behavioral performance often peak at intermediate arousal rather than at the highest arousal.

Key findings

  • Pupil diameter tracks rapid cortical state: dilation is linked to suppression of low-frequency (about 2–10 Hz) membrane-potential/LFP rhythms and enhancement of gamma (30–80 Hz) activity, while constriction is linked to stronger low-frequency activity below a critical pupil size. This relationship appears across visual, somatosensory, and auditory cortex and also with hippocampal sharp-wave ripples.
  • State modulates sensory responses along five dimensions: response magnitude, signal-to-noise ratio, timing, variability, and noise correlations. For example, visual cortical responses are enhanced by locomotion, auditory cortical responses are largest at intermediate arousal, and noise correlations are lower during locomotion than during quiescence.
  • Behavioral performance can follow an inverted-U with arousal. In an auditory tone-in-noise detection task, mice showed maximal hit rate, shortest lick latency, best discrimination, and least bias at intermediate pupil diameters; further dilation, including during locomotion, reduced performance. Human oddball and primate saccade studies similarly showed optimal performance at mid-level pupil-indexed arousal.
  • Mice exhibit frequent state changes: the review cites more than 100 sleep–wake transitions per 24 hours, with average sleep or waking bouts lasting under 10 minutes. It also contrasts two models of arousal-dependent cortical control: a sigmoidal model and a U-shaped model of average membrane potential versus arousal.

Methods

  • Narrative review synthesizing recent work, largely in behaving mice, with relevant primate and human literature; no new primary data are reported.
  • Evidence discussed includes intracellular membrane-potential recordings, local field potentials, single-unit spiking, pupillometry, locomotion/whisking monitoring, and behavioral tasks such as auditory signal detection, visual discrimination, and saccade tasks.
  • Main analysis is conceptual integration across studies, including proposed sigmoidal and U-shaped models of arousal-dependent cortical activity and a disinhibitory VIP–SOM/PV circuit mechanism.

Limitations

  • The paper states that the study of waking substates is still in its infancy, with no consensus on how many substates exist or how to define them; it also says the review cannot account for all or even many waking substates.
  • The paper states that the precise mechanisms controlling state are not fully known, the pathways linking pupil diameter to arousal are unknown, and reasons for cell-to-cell variation in arousal responses remain unresolved.
  • Evident from the text: this is a narrative review rather than a systematic or quantitative synthesis, and many conclusions rely on cited studies in head-fixed mice, so generality across species, tasks, and cortical areas is not fully established.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.neuron.2015.09.012

Cites

173 of 173 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 299 cited sources checked · 0 supported · 0 flagged

Show all 113 claims

  • It may state two separate claims as one. 66
  • Probably not a substantial claim. 9
73 of 73
  • The paper relies on this without support.

    The conventional division of wakefulness into quiet and active periods fails to capture the full granularity of behavior and neural activity.

    “the prevailing approach in rodent research of dividing wakefulness into quiet and active awake periods, depending on whether animals are standing still or exhibiting exploratory movements (e.g. whisking or locomotion) almost certainly fails to capture the full granularity of both behavior and neural activity”
  • The paper relies on this without support.

    Arousal and locomotion can be partially separated in head-fixed mice by monitoring treadmill activity, pupil size, and neural activity.

    “Monitoring treadmill activity, pupil size, and neural activity has made it possible to partially disambiguate the effects of locomotion from the changes in arousal”
  • The paper relies on this without support.

    Firing rates are highly dependent on behavioral state.

    “Because firing rates are highly dependent on behavioral state, pooling neuronal responses across states is likely to dramatically increase observed neuronal variability.”
  • The paper relies on this without support.

    The waking state constantly fluctuates, even on a second by second basis.

    “Given that the waking state constantly fluctuates, even on a second by second basis, it is imperative to examine the relationship between the state of the brain/animal and its performance on behavioral tasks.”
  • The paper relies on this without support.

    Arousal, stress, and their effects on behavior are interrelated but separable.

    “Arousal, stress, and their effects on behavior are interrelated but separable.”
  • The paper relies on this without support.

    In rodents, the density and prevalence of low frequency rhythms increase over the course of the 12 hour active period since lights off.

    “For example, in rodents the density and prevalence of low frequency rhythms (Figure 1B) increase over the course of the 12 hour active period since lights off.”
  • The paper relies on this without support.

    The awake brain exhibits ever-changing activity patterns, many of which are state changes.

    “neuroscientists are faced with ever-changing patterns of activity in the awake brain, many of which take the form of state changes”
7 of 7

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.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Reaction-time curve conflicts with the stated performance pattern

    Minor

    The text calls the relationship between reaction time and similarity to average ECoG activity an inverted U, but then says performance is best near the average and worsens with deviations in either direction. If better performance means shorter reaction time, the described reaction-time pattern has a minimum near the average, not a maximum.

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Metadata

Authors
McGinley, M. J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C. R., Tolias, A. S., Cardin, J. A., & McCormick, D. A.
Affiliations
  • Yale University (US)
  • Baylor College of Medicine (US)
Corresponding author
  • Tolias, Andreas S.
  • Cardin, Jessica A.
  • McCormick, David A.
Venue
Neuron
Publisher
Elsevier BV
Published
2015-09
Volume
87
Issue
6
Pages
1143-1161
ISSN
0896-6273 (ISSN-L)
OpenAlex
W2116679324
Cited by
801
References
176
Citations by year
2026: 71 · 2025: 61 · 2024: 88 · 2023: 93 · 2022: 86 · 2021: 100 · 2020: 76 · 2019: 69 · 2018: 67 · 2017: 52 · 2016: 36 · 2015: 2
FWCI
5.15
Type
journal article
Language
en
Open access
Locations
  • Neuron (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Institute of Museum and Library Services
  • McKnight Foundation
  • Yale University
  • National Alliance for Research on Schizophrenia and Depression
  • National Institutes of Health (5R01N2026143, 1dp1od008301-01)
  • U.S. Public Health Service (5R01N2026143)
  • National Institute of Mental Health (F30MH095440)
  • National Eye Institute (DP1EY023176, T32 EY07001, EY022951, P30EY002520, R00EY018407, T32EY007001, R01EY022951)
  • National Institute on Deafness and Other Communication Disorders (R01DC015803, F32DC012449)
  • National Institute of General Medical Sciences (T32GM007330)
  • National Institute of Neurological Disorders and Stroke (R01NS026143)
  • National Institute of Biomedical Imaging and Bioengineering (T32EB006350)
  • National Center for Chronic Disease Prevention and Health Promotion (DP1EY023176)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
pupillometry, sensory coding, spontaneous neuronal activity, behavioral performance, neural reliability, awake behaving animals, sensory noise
MeSH terms
Action Potentials, Animals, Brain, Humans, Motor Activity, Nerve Net, Neurons, Time Factors, Wakefulness

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