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Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection
Open accessAbstract
The neural correlates of optimal states for signal detection task performance are largely unknown. One hypothesis holds that optimal states exhibit tonically depolarized cortical neurons with enhanced spiking activity, such as occur during movement. We recorded membrane potentials of auditory cortical neurons in mice trained on a challenging tone-in-noise detection task while assessing arousal with simultaneous pupillometry and hippocampal recordings. Arousal measures accurately predicted multiple modes of membrane potential activity, including rhythmic slow oscillations at low arousal, stable hyperpolarization at intermediate arousal, and depolarization during phasic or tonic periods of hyper-arousal. Walking always occurred during hyper-arousal. Optimal signal detection behavior and sound-evoked responses, at both sub-threshold and spiking levels, occurred at intermediate arousal when pre-decision membrane potentials were stably hyperpolarized. These results reveal a cortical physiological signature of the classically observed inverted-U relationship between task performance and arousal and that optimal detection exhibits enhanced sensory-evoked responses and reduced background synaptic activity.
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In the citing paper
“By employing head-restrained preparations, a growing body of work has demonstrated that behavioral state fluctuations have a strong impact on neuronal gain and coding properties in primary sensory areas36,43,66–70 and modulate neuronal firing within brain-wide neuronal networks44,71–74.”
From Place and behavioral modulation of hippocampal neurons during immobility (2026) · cites this paper as [67]
In the citing paper
“These changes are sometimes coupled with overt movements such as whisking or locomotion”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“and can also be predicted with high accuracy by changes in pupil diameter or muscle tone while animals are sitting quietly”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“It has become increasingly clear that the waking state contains continuous transitions between numerous sub-states that are associated with significant changes in sensory-motor processing and behavioral performance”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“New animal studies reveal that rapid, ongoing, and behaviorally-relevant variations in waking state are not the exception, but rather the rule”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“This work has revealed that spontaneous and sensory-evoked activity varies continuously and rapidly during wakefulness.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“These variations in state exert an effect on sensory evoked neural activity that can be as significant as variations in the sensory stimulus itself”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“During periods of quiet wakefulness (lacking overt movement), large low-frequency (< 10 Hz) fluctuations in the synaptic activity of somatosensory, visual, and auditory cortical neurons are frequently observed, and these oscillations are strongly suppressed by the initiation of whisking or locomotion”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“The transition from the quiet awake state to movement (locomotion and/or whisking) in head-fixed mice is always preceded by a strong suppression of cortical slow rhythms at the action potential, membrane potential, and field potential, levels”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Recently, intracellular membrane potential (Vm) and local field potential (LFP) recordings from cortical neurons collected simultaneously with pupil diameter in head-fixed, spontaneously locomoting or whisking mice revealed a marked relationship between 1) pupil size, 2) low frequency (2–10 Hz) fluctuations in membrane potential/LFP, and 3) exploratory behaviors (whisking and locomotion)”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“At these intermediate arousal levels, the average membrane potential of infragranular cortical neurons is at its most hyperpolarized, and least variable, level”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“has been observed in visual, somatosensory, and auditory cortical areas”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Interestingly, the initiation of locomotion is preceded by a substantial increase in pupil diameter (Figures 2–4), concurrent with the reduction of low-frequency cortical local field potential synchronization and reduced hippocampal sharp wave and ripple oscillations.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“At low levels of arousal, cortical neurons exhibit a high density of large, slow, rhythmic fluctuations in synaptic activity”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Likewise, low-frequency cortical activity is enhanced during pupillary constriction, especially below a critical level of pupil diameter.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Even in the absence of movement, increases in pupil diameter (dilation) were found to be associated with increases in cortical activation and suppression of low frequency rhythms.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“For example, the presence of these different types of synaptic and membrane potential dynamics at low and high arousal states results in a U-shaped relationship between arousal and average cortical membrane potential in deep layer neurons of the auditory cortex”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Additionally, the relationship between cortical membrane potential and pupil diameter is influenced by the presence of slow oscillations at low arousal states and strong barrages of synaptic activity and/or tonic depolarization at high arousal states”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“The correlation between pupil diameter and the rate of sharp-wave ripples in the hippocampus (Figure 2D), further underscores the generality of this effect.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Increases in arousal from low to intermediate levels result in a suppression of these low (1–10 Hz) frequency activities, a decrease in membrane potential fluctuations, and thus a decrease in spontaneous activity”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Increases from intermediate to high arousal associated with brief (microdilations) or long-lasting increases in pupil diameter (often associated with locomotion), result in a depolarization of the membrane potential and increases in high-frequency membrane potential fluctuations from synaptic barrages”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“In addition to predicting changes in oscillatory network dynamics, changes in pupil diameter are reliably correlated with the average subthreshold membrane potential of at least some subtypes of neurons of the cortex, even in the absence of overt movement”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“The transition from sitting quietly to exploratory behaviors (whisking and locomotion) is intertwined with changes in arousal tracked by pupillometry.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“As described above, even in the quiescent state devoid of overt movements, pupil dilation is associated with suppression of low-frequency, and enhancement of gamma-frequency, cortical network activities that can occur as either a rapid switching (Figure 2A) or as a graded and continuous change”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“During the period of movement (whisking or locomotion), neurons in somatosensory, visual, motor, and auditory cortex maintain an elevated firing rate, the degree of which varies across cell types.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“For example, a subset of layer 5 pyramidal neurons throughout the cortex is activated by movement”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Following the cessation of locomotion, head-fixed mice typically exhibit a transition from high arousal to moderate or low arousal, as measured by pupil diameter, over a period of seconds or longer.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“This gradual decrease in pupil diameter is associated with an increase in the prevalence of low-frequency oscillations”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“In contrast, in auditory cortex, the S/N peaks at intermediate arousal levels and may decrease strongly at high arousal levels and during locomotion”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Behavioral state is an important determinant of both the temporal precision of sensory responses and gamma-band synchronization.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Spontaneous or causally induced increases in arousal are associated with a monotonic increase in gamma-band synchronization both in terms of local field potential and relative membrane potential power (Lee et al., 2014; Lima et al., 2011; McGinley et al., 2015; Reimer et al., 2014; Vinck et al., 2015), and spike-local field potential phase locking”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Changes in arousal result in marked multiplicative gain modulation of auditory cortical responses, with the largest responses being evoked at intermediate levels of arousal”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Indeed, compensating for these rapid variations in state can significantly reduce variability in the amplitude and timing of both sensory evoked and behavioral responses (McGinley et al., 2015; Reimer et al., 2014).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“in L5 of auditory cortex, variability is increased during low and high arousal (either during quiet or active waking), but reduced at intermediate arousal (McGinley et al., 2015).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Monitoring of state through pupillometry reveals rapid (seconds) and continuous changes (Figure 2–4), indicating that a significant fraction of trial-to-trial variability in sensory evoked responses may be the result of variations in state.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“All three measures exhibited an inverted U relationship with arousal, with the largest, most reliable, and highest S/N ratio responses occurring at the same intermediate pupil diameters that revealed optimal performance on the auditory tone-in-complex sound detection task (Figure 7).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Importantly, the performance of mice on this auditory detection task was highly state dependent, with maximal hit rate, shortest lick latency, maximal discrimination, and least behavioral bias all occurring at intermediate pupil diameters (Figure 7C).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“At present, intracellular recordings from cortical neurons support both the sigmoidal and U model of arousal (e.g. Figure 1–3).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“In contrast to the sigmoidal model of activation, intracellular recordings in awake behaving rodents can reveal a pattern that is more consistent with a U-shaped dependence of average membrane potential and cortical activation on arousal (McGinley et al., 2015) (Figures 3C, 8C).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Interestingly, locomotion, as a sub-state of hyper-arousal, was associated with a strong decrease in behavioral performance, in comparison to intermediate arousal levels, which always occurred during stillness.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Increases in arousal from low to intermediate, as indicated by increases in pupil diameter from small to medium, were associated with a large increase in task performance, while further increases in pupil diameter from intermediate to large were associated with a strong decrement in performance, whether or not the animal was walking at these large pupil diameters (Figure 7C).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Interestingly, sound evoked responses in the auditory cortex also exhibited a strong relationship between arousal (pupil diameter) and the gain, amplitude, and reliability of these responses (Figure 7D).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
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Citations
Cites
The relation of strength of stimulus to rapidity of habit formation
Yerkes, R. M., & Dodson, J. D. (1908) · Journal of Comparative Neurology and Psychology · doi:10.1002/cne.920180503 · Cited by 6,911
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472
The pupil as a measure of emotional arousal and autonomic activation
Bradley, M. M., Miccoli, L., Escrig, M. A., & Lang, P. J. (2008) · Psychophysiology · doi:10.1111/j.1469-8986.2008.00654.x · Cited by 1,919
Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity
van Vreeswijk, C., & Sompolinsky, H. (1996) · Science · doi:10.1126/science.274.5293.1724 · Cited by 1,831
Inverted-U–Shaped Dopamine Actions on Human Working Memory and Cognitive Control
Cools, R., & D'Esposito, M. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.03.028 · Cited by 1,673
Modulation of visual responses by behavioral state in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.033 · Cited by 1,483 · cited 6×
“Are the effects of arousal similar across cortical areas? In studies of the visual cortex, locomotion is associated with enhanced sensory-evoked visual responses (over non-locomotion) (Bennett et al., 2013; Niell and Stryker, 2010; Polack et al., 2013), while in auditory cortex locomotion results in significant suppre…”
BackgroundResultInfluentialBalanced inhibition underlies tuning and sharpens spike timing in auditory cortex
Wehr, M., & Zador, A. M. (2003) · Nature · doi:10.1038/nature02116 · Cited by 1,413
Natural waking and sleep states: a view from inside neocortical neurons
Steriade, M., Timofeev, I., & Grenier, F. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.85.5.1969 · Cited by 1,296
Cortical interneurons that specialize in disinhibitory control
Pi, H.-J., Hangya, B., Kvitsiani, D., Sanders, J. I., Huang, Z. J., & Kepecs, A. (2013) · Nature · doi:10.1038/nature12676 · Cited by 1,136
The Asynchronous State in Cortical Circuits
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D. (2010) · Science · doi:10.1126/science.1179850 · Cited by 1,106
Flagged first, then by citations
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Sorted by citations
Items 101–200 of 729
Calcium Imaging of Basal Forebrain Activity during Innate and Learned Behaviors
Harrison, T. C., Pinto, L., Brock, J. R., & Dan, Y. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00036 · Cited by 101
Dynamics of auditory cortical activity during behavioural engagement and auditory perception
Carcea, I., Insanally, M. N., & Froemke, R. C. (2017) · Nature Communications · doi:10.1038/ncomms14412 · Cited by 97
van Kempen, J., Loughnane, G. M., Newman, D. P., Kelly, S. P., Thiele, A., O’Connell, R. G., & Bellgrove, M. A. (2019) · eLife · doi:10.7554/elife.42541 · Cited by 92
Brain-wide interactions during hippocampal sharp wave ripples
Nitzan, N., Swanson, R. A., Schmitz, D., & Buzsáki, G. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2200931119 · Cited by 99
Dedicated Hippocampal Inhibitory Networks for Locomotion and Immobility
Arriaga, M., & Han, E. B. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1076-17.2017 · Cited by 95
Catecholaminergic manipulation alters dynamic network topology across cognitive states
Shine, J. M., van den Brink, R. L., Hernaus, D., Nieuwenhuis, S., & Poldrack, R. A. (2018) · Network Neuroscience · doi:10.1162/netn_a_00042 · Cited by 95
Malina, K. C.-K., Tsivourakis, E., Kushinsky, D., Apelblat, D., Shtiglitz, S., Zohar, E., Sokoletsky, M., Tasaka, G., Mizrahi, A., Lampl, I., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.05.001 · Cited by 95
Structure and function of axo-axonic inhibition
Schneider-Mizell, C. M., Bodor, A. L., Collman, F., Brittain, D., Bleckert, A. A., Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., et al. (2021) · eLife · doi:10.7554/elife.73783 · Cited by 115
Collins, L. N., Francis, J., Emanuel, B., & McCormick, D. A. (2023) · eLife · doi:10.7554/elife.81826 · Cited by 87
Self-citationNeurobehavioral meaning of pupil size
Grujic, N., Polanía, R., & Burdakov, D. (2024) · Neuron · doi:10.1016/j.neuron.2024.05.029 · Cited by 108
Catecholamine-Mediated Increases in Gain Enhance the Precision of Cortical Representations
Warren, C. M., Eldar, E., van den Brink, R. L., Tona, K., van der Wee, N. J., Giltay, E. J., van Noorden, M. S., Bosch, J. A., Wilson, R. C., Cohen, J. D., et al. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3475-15.2016 · Cited by 87
Complexity and 1/f slope jointly reflect brain states
Medel, V., Irani, M., Crossley, N. A., Ossandón, T., & Boncompte, G. (2023) · Scientific Reports · doi:10.1038/s41598-023-47316-0 · Cited by 91
An inhibitory pull–push circuit in frontal cortex
García‐Junco‐Clemente, P., Ikrar, T., Tring, E., Xu, X., Ringach, D. L., & Trachtenberg, J. T. (2017) · Nature Neuroscience · doi:10.1038/nn.4483 · Cited by 89
Rasmussen, R. N., Nicholas, E., Petersen, N., Dietz, A. G., Xu, Q., Sun, Q., & Nedergaard, M. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.06.082 · Cited by 86
Control of locomotor speed, arousal, and hippocampal theta rhythms by the nucleus incertus
Lu, L., Ren, Y., Yu, T., Liu, Z., Wang, S., Tan, L., Zeng, J., Feng, Q., Lin, R., Liu, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14116-y · Cited by 84
A pupil size response model to assess fear learning
Korn, C. W., Staib, M., Tzovara, A., Castegnetti, G., & Bach, D. R. (2016) · Psychophysiology · doi:10.1111/psyp.12801 · Cited by 84
Contextual signals in visual cortex
Khan, A. G., & Hofer, S. B. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.05.003 · Cited by 83
Sniff-synchronized, gradient-guided olfactory search by freely moving mice
Findley, T. M., Wyrick, D. G., Cramer, J. L., Brown, M. A., Holcomb, B., Attey, R., Yeh, D., Monasevitch, E., Nouboussi, N., Cullen, I., et al. (2021) · eLife · doi:10.7554/elife.58523 · Cited by 84
Auditory Corticothalamic Neurons Are Recruited by Motor Preparatory Inputs
Clayton, K. K., Williamson, R. S., Hancock, K. E., Tasaka, G., Mizrahi, A., Hackett, T. A., & Polley, D. B. (2020) · Current Biology · doi:10.1016/j.cub.2020.10.027 · Cited by 86
Prestimulus influences on auditory perception from sensory representations and decision processes
Kayser, S. J., McNair, S. W., & Kayser, C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1524087113 · Cited by 85
Meijer, G. T., Montijn, J. S., Pennartz, C. M. A., & Lansink, C. S. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0468-17.2017 · Cited by 82
Sensation during Active Behaviors
Busse, L., Cardin, J. A., Chiappe, M. E., Halassa, M. M., McGinley, M. J., Yamashita, T., & Saleem, A. B. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1828-17.2017 · Cited by 84
Self-citationCompeting Neural Ensembles in Motor Cortex Gate Goal-Directed Motor Output
Zagha, E., Ge, X., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.044 · Cited by 84
Self-citationBeaman, C. B., Eagleman, S. L., & Dragoi, V. (2017) · Nature Communications · doi:10.1038/s41467-017-01030-4 · Cited by 80
Role of the locus coeruleus arousal system in cognitive control
Grueschow, M., Kleim, B., & Ruff, C. C. (2020) · Journal of Neuroendocrinology · doi:10.1111/jne.12890 · Cited by 83
An Excitatory Circuit in the Perioculomotor Midbrain for Non-REM Sleep Control
Zhang, Z., Zhong, P., Hu, F., Barger, Z., Ren, Y., Ding, X., Li, S., Weber, F., Chung, S., Palmiter, R. D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.041 · Cited by 81
Offline memory consolidation during waking rest
Wamsley, E. J. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00072-w · Cited by 84
Noradrenergic and cholinergic systems take centre stage in neuropsychiatric diseases of ageing
Orlando, I. F., Shine, J. M., Robbins, T. W., Rowe, J. B., & O’Callaghan, C. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105167 · Cited by 81
Pfeffer, T. A., Ponce‐Alvarez, A., Meindertsma, T., Gahnstrom, C. J., van den Brink, R. L., Nolte, G., Tsetsos, K., Engel, A. K., Deco, G., & Donner, T. H. (2021) · Science Advances · doi:10.1126/sciadv.abf5620 · Cited by 73
Reimann, H. M., & Niendorf, T. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00008 · Cited by 75
Vinck, M. A., & Bosman, C. A. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00035 · Cited by 74
A solid frame for the window on cognition: Modeling event-related pupil responses
Korn, C. W., & Bach, D. R. (2016) · Journal of Vision · doi:10.1167/16.3.28 · Cited by 75
Layer 5 Circuits in V1 Differentially Control Visuomotor Behavior
Tang, L., & Higley, M. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.10.014 · Cited by 72
Cortical State Fluctuations during Sensory Decision Making
Jacobs, E. A. K., Steinmetz, N. A., Peters, A. J., Carandini, M., & Harris, K. D. (2020) · Current Biology · doi:10.1016/j.cub.2020.09.067 · Cited by 69
Williamson, R. S., Hancock, K. E., Shinn‐Cunningham, B. G., & Polley, D. B. (2015) · Current Biology · doi:10.1016/j.cub.2015.05.045 · Cited by 70
Noradrenergic Responsiveness Supports Selective Attention across the Adult Lifespan
Dahl, M. J., Mather, M., Sander, M. C., & Werkle-Bergner, M. (2020) · The Journal of Neuroscience · doi:10.1523/jneurosci.0398-19.2020 · Cited by 72
Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas
Avitan, L., & Stringer, C. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.019 · Cited by 71
Joshi, S., & Gold, J. I. (2022) · eLife · doi:10.7554/elife.63490 · Cited by 71
Pupillary Correlates of Fluctuations in Sustained Attention
Nash Unsworth, A., Robison, M. K., & Miller, A. L. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01251 · Cited by 77
Stereotypic wheel running decreases cortical activity in mice
Fisher, S. P., Cui, N., McKillop, L. E., Gemignani, J., Bannerman, D. M., Oliver, P. L., Peirson, S. N., & Vyazovskiy, V. V. (2016) · Nature Communications · doi:10.1038/ncomms13138 · Cited by 70
“Arguably, high-speed stereotypic running and goal-directed or exploratory behaviours are not only associated with a different type and amount of locomotion, but also with differences in the levels of arousal, which can in turn modulate sensory processing and behavioural performance 6,54 .”
Arousal dependent modulation of thalamo-cortical functional interaction
Stitt, I., Zhou, Z. C., Radtke‐Schuller, S., & Frӧhlich, F. (2018) · Nature Communications · doi:10.1038/s41467-018-04785-6 · Cited by 72
O’Callaghan, C., Walpola, I. C., & Shine, J. M. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0699 · Cited by 77
From pre-processing to advanced dynamic modeling of pupil data
Fink, L., Simola, J., Tavano, A., Lange, E. B., Wallot, S., & Laeng, B. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02098-1 · Cited by 80
Dridi, H., Liu, Y., Reiken, S. R., Liu, X., Argyrousi, E. K., Yuan, Q., Miotto, M., Sittenfeld, L. R., Meddar, A., Soni, R. K., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01377-6 · Cited by 69
Noradrenergic ensemble-based modulation of cognition over multiple timescales
Totah, N. K. B., Logothetis, N. K., & Eschenko, O. (2018) · Brain Research · doi:10.1016/j.brainres.2018.12.031 · Cited by 75
Both a Gauge and a Filter: Cognitive Modulations of Pupil Size
Ebitz, R. B., & Moore, T. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2018.01190 · Cited by 79
Pais‐Roldán, P., Takahashi, K., Sobczak, F., Chen, Y., Zhao, X., Zeng, H., Jiang, Y., & Yu, X. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1909937117 · Cited by 67
Auditory cortex shapes sound responses in the inferior colliculus
Blackwell, J. M., Lesicko, A. M., Rao, W., De Biasi, M., & Geffen, M. N. (2020) · eLife · doi:10.7554/elife.51890 · Cited by 68
Onorato, I., Neuenschwander, S., Hoy, J. L., Lima, B., Rocha, K.-S., Broggini, A. C., Uran, C., Spyropoulos, G., Klon-Lipok, J., Womelsdorf, T., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.039 · Cited by 65
Arousal as a universal embedding for spatiotemporal brain dynamics
Raut, R. V., Rosenthal, Z. P., Wang, X., Miao, H., Zhang, Z., Lee, J., Raichle, M. E., Bauer, A. Q., Brunton, S. L., Brunton, B. W., et al. (2025) · Nature · doi:10.1038/s41586-025-09544-4 · Cited by 91
Arena, A., Comolatti, R., Thon, S., Casali, A. G., & Storm, J. F. (2021) · eneuro · doi:10.1523/eneuro.0343-20.2021 · Cited by 66
Movement and VIP Interneuron Activation Differentially Modulate Encoding in Mouse Auditory Cortex
Bigelow, J., Morrill, R. J., DeKloe, J., & Hasenstaub, A. R. (2019) · eNeuro · doi:10.1523/eneuro.0164-19.2019 · Cited by 66
Spectral signature and behavioral consequence of spontaneous shifts of pupil-linked arousal in human
Podvalny, E., King, L. E., & He, B. J. (2021) · eLife · doi:10.7554/elife.68265 · Cited by 66
Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior
Benisty, H., Barson, D., Moberly, A. H., Lohani, S., Tang, L., Coifman, R. R., Crair, M. C., Mishne, G., Cardin, J. A., & Higley, M. J. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01498-y · Cited by 65
Three brain states in the hippocampus and cortex
Kay, K., & Frank, L. M. (2018) · Hippocampus · doi:10.1002/hipo.22956 · Cited by 69
Eye activity tracks task-relevant structures during speech and auditory sequence perception
Jin, P., Zou, J., Zhou, T., & Ding, N. (2018) · Nature Communications · doi:10.1038/s41467-018-07773-y · Cited by 65
Robert, B., Kimchi, E. Y., Watanabe, Y., Chakoma, T. L., Jing, M., Li, Y., & Polley, D. B. (2021) · eLife · doi:10.7554/elife.69514 · Cited by 64
Wide-Field Calcium Imaging of Dynamic Cortical Networks during Locomotion
West, S. L., Aronson, J. D., Popa, L. S., Feller, K. D., Carter, R. E., Chiesl, W. M., Gerhart, M. L., Shekhar, A. C., Ghanbari, L., Kodandaramaiah, S. B., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab373 · Cited by 64
Task-evoked pupil responses reflect internal belief states
Colizoli, O., de Gee, J. W., Urai, A. E., & Donner, T. H. (2018) · Scientific Reports · doi:10.1038/s41598-018-31985-3 · Cited by 67
Einstein, M., Polack, P., Tran, D. T., & Golshani, P. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3868-16.2017 · Cited by 62
Spatial attention enhances network, cellular and subthreshold responses in mouse visual cortex
Speed, A., Rosario, J. D., Mikail, N., & Haider, B. (2020) · Nature Communications · doi:10.1038/s41467-020-14355-4 · Cited by 57
Tracking working memory maintenance with pupillometry
Nash Unsworth, A., & Robison, M. K. (2017) · Attention Perception & Psychophysics · doi:10.3758/s13414-017-1455-x · Cited by 59
Artoni, P., Piffer, A., Vinci, V., LeBlanc, J. J., Nelson, C. A., Hensch, T. K., & Fagiolini, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820847116 · Cited by 57
Integration of locomotion and auditory signals in the mouse inferior colliculus
Yang, Y., Lee, J., & Kim, G. (2020) · eLife · doi:10.7554/elife.52228 · Cited by 67
Ripple-selective GABAergic projection cells in the hippocampus
Szabo, G. G., Farrell, J. S., Dudok, B., Hou, W., Ortiz, A. L., Varga, C., Moolchand, P., Gülsever, C. İ., Gschwind, T., Dimidschstein, J., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.04.002 · Cited by 61
Vinck, M. A., Bos, J. J., Mourik-Donga, L. A. V., Oplaat, K. T., Klein, G. A., Jackson, J. C., Gentet, L. J., & Pennartz, C. M. A. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00187 · Cited by 60
Visual search performance in infants associates with later ASD diagnosis
Cheung, C. H. M., Bedford, R., Johnson, M. H., Charman, T., & Gliga, T. (2016) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2016.09.003 · Cited by 63
Acute Exercise Modulates Feature-selective Responses in Human Cortex
Bullock, T., Elliott, J. C., Serences, J. T., & Giesbrecht, B. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01082 · Cited by 56
Neske, G. T., Nestvogel, D., Steffan, P. J., & McCormick, D. A. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1226-18.2019 · Cited by 59
Self-citationLost in translation: How to upgrade fear memory research
Flores, Á., Fullana, M. À., Soriano-Mas, C., & Andero, R. (2018) · Molecular Psychiatry · doi:10.1038/s41380-017-0006-0 · Cited by 59
Top-down coordination of local cortical state during selective attention
van Kempen, J., Gieselmann, M. A., Boyd, M., Steinmetz, N. A., Moore, T., Engel, T. A., & Thiele, A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.013 · Cited by 61
Sequential transmission of task-relevant information in cortical neuronal networks
Francis, N. A., Mukherjee, S., Koçillari, L., Panzeri, S., Babadi, B., & Kanold, P. O. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110878 · Cited by 60
Lee, C. R., & Margolis, D. J. (2016) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00200 · Cited by 59
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Rodgers, C. C., Nogueira, R., Pil, B. C., Greeman, E. A., Park, J. M., Hong, Y. K., Fusi, S., & Bruno, R. M. (2021) · Neuron · doi:10.1016/j.neuron.2021.05.019 · Cited by 59
Liang, F., Li, H., Chou, X.-L., Zhou, M., Zhang, N. K., Xiao, Z., Zhang, K. K., Tao, H. W., & Zhang, L. I. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy260 · Cited by 60
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Burlingham, C. S., Mirbagheri, S., & Heeger, D. J. (2022) · Science Advances · doi:10.1126/sciadv.abi9979 · Cited by 57
Beerendonk, L., Mejías, J. F., Nuiten, S. A., de Gee, J. W., Fahrenfort, J. J., & van Gaal, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312898121 · Cited by 60
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Chang, H., Tang, W., Wulf, A. M., Nyasulu, T., Wolf, M. E., Fernández‐Ruiz, A., & Oliva, A. (2025) · Nature · doi:10.1038/s41586-024-08340-w · Cited by 62
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Liang, L., Fratzl, A., Reggiani, J. D. S., Mansour, O. E., Chen, C., & Andermann, M. L. (2020) · Current Biology · doi:10.1016/j.cub.2020.07.065 · Cited by 54
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See, J. Z., Atencio, C. A., Sohal, V. S., & Schreiner, C. E. (2018) · eLife · doi:10.7554/elife.35587 · Cited by 57
Harnessing behavioral diversity to understand neural computations for cognition
Musall, S., Urai, A. E., Sussillo, D., & Churchland, A. K. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.011 · Cited by 59
Single-neuron firing cascades underlie global spontaneous brain events
Liu, X., Leopold, D. A., & Yang, Y. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105395118 · Cited by 53
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von Trapp, G., Buran, B. N., Sen, K., Semple, M. N., & Sanes, D. H. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1302-16.2016 · Cited by 52
Synchronous activity patterns in the dentate gyrus during immobility
Pofahl, M., Nikbakht, N., Haubrich, A. N., Nguyen, T. M., Masala, N., Distler, F. J., Braganza, O., Macke, J. H., Ewell, L. A., Gölcük, K., et al. (2021) · eLife · doi:10.7554/elife.65786 · Cited by 50
Contextual modulation of sound processing in the auditory cortex
Angeloni, C. F., & Geffen, M. N. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.10.012 · Cited by 50
Schriver, B. J., Bagdasarov, S., & Wang, Q. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00290.2018 · Cited by 58
Decision-making dynamics are predicted by arousal and uninstructed movements
Hulsey, D., Zumwalt, K., Mazzucato, L., McCormick, D. A., & Jaramillo, S. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113709 · Cited by 57
Self-citationEnhanced brainstem and cortical encoding of sound during synchronized movement
Nozaradan, S., Schönwiesner, M., Caron-Desrochers, L., & Lehmann, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.015 · Cited by 56
Olcese, U., Lohuis, M. N. O., & Pennartz, C. M. A. (2018) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00049 · Cited by 53
Arousal regulates frequency tuning in primary auditory cortex
Lin, P.-A., Asinof, S. K., Edwards, N. J., & Isaacson, J. S. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1911383116 · Cited by 51
Brain dynamics for confidence-weighted learning
Meyniel, F. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007935 · Cited by 55
Pupil size reveals arousal level fluctuations in human sleep
Carro-Domínguez, M., Huwiler, S., Oberlin, S., Oesch, T. L., Badii, G., Lüthi, A., Wenderoth, N., Meissner, S. N., & Lustenberger, C. (2025) · Nature Communications · doi:10.1038/s41467-025-57289-5 · Cited by 41
Lörincz, M. L., & Adamantidis, A. (2016) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2016.09.003 · Cited by 54
Reflections of action in sensory cortex
Schneider, D. M. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.02.004 · Cited by 50
Shahsavarani, S., Thibodeaux, D. N., Xu, W., Kim, S. H., Lodgher, F., Nwokeabia, C., Cambareri, M. K., Yagielski, A. J., Zhao, H., Handwerker, D. A., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112527 · Cited by 52
Cortical State Fluctuations across Layers of V1 during Visual Spatial Perception
Speed, A., Rosario, J. D., Burgess, C., & Haider, B. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.045 · Cited by 47
Attention lapses and behavioural microsleeps during tracking, psychomotor vigilance, and dual tasks
Buckley, R. J., Helton, W. S., Innes, C., Dalrymple‐Alford, J. C., & Jones, R. D. (2016) · Consciousness and Cognition · doi:10.1016/j.concog.2016.09.002 · Cited by 55
Neuromodulation of Neural Oscillations in Health and Disease
Weiss, E., Kann, M., & Wang, Q. (2023) · Biology · doi:10.3390/biology12030371 · Cited by 57
Cortical signatures of wakeful somatosensory processing
Song, C., Piscopo, D. M., Niell, C. M., & Knöpfel, T. (2018) · Scientific Reports · doi:10.1038/s41598-018-30422-9 · Cited by 54
Motor output, neural states and auditory perception
Reznik, D., & Mukamel, R. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.10.021 · Cited by 47
Metadata
- Authors
- McGinley, M. J., David, S. V., & McCormick, D. A.
- Affiliations
- Yale University (US)
- Oregon Health & Science University (US)
- Corresponding author
- McGinley, Matthew J.
- McCormick, David A.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 87
- Issue
- 1
- Pages
- 179-192
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC4631312
- OpenAlex
- W1519578210
- Cited by
- 728
- References
- 57
- Citations by year
- 2026: 58 · 2025: 73 · 2024: 80 · 2023: 89 · 2022: 67 · 2021: 73 · 2020: 61 · 2019: 53 · 2018: 64 · 2017: 52 · 2016: 48 · 2015: 10
- FWCI
- 34.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Public Health Service (5R01N2026143)
- National Institute on Deafness and Other Communication Disorders (R01DC015803, 1F132DC012449, F32DC012449, R00DC010439)
- National Institute of Neurological Disorders and Stroke (R01NS026143)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- signal detection, arousal, inverted U-shaped relationship, auditory cortex, tone-in-noise detection, pupillometry, slow oscillations, hyperpolarization, depolarization, synaptic activity
- MeSH terms
- Animals, Arousal, Auditory Cortex, Auditory Perception, Membrane Potentials, Neurons, Signal Detection, Psychological, Mice
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