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• W1519578210 abstract "•Optimal performance of mice on an auditory detection task occurs at moderate arousal•Large, reliable sub- and supra-threshold sensory responses occur at moderate arousal•Hyperpolarized, quiescent membrane potentials precede correct detection events•Pupil diameter predicts cortical dynamic states governing detection task performance 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.Video AbstracteyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiIxYjY0MmM1ZmM5NWU0NTAzMmFmMGRhNDEzOTY3M2Y1MCIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjc4ODk3MjY0fQ.ZQiobpZGiv992WQY0A5bba_6bT7s5q8kL7IOHHjJ8ZtdIrazh5-frsCoOtEHjej2T3QvAqFbdcHPSb4CpEMNNiMYqpxRlFxrFr2xiblxefKzkDVFwMnLOWyUv6zYefP2uaLdIJf39osOiDhNDfKGoTQcGqKGozO_3MUYTQvQ9DM5wbnSbWqyvbovNyNQi2OasEZsG7pSmTF0YJYCuwjS3nHNeYfx0E216l87iddZBrKc71YOhfykZgYpHH9wPLrRB_w_xXqxDa1xvfr_4PNuu5ZTVm3b64IOuMJE09XJNEt4e-_4DwaBXyySFMXiHJnhcyWkmfI7HgP4CVUQGJMHsA(mp4, (60.15 MB) Download video 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. The cortical subthreshold membrane potential and synaptic dynamics underlying optimal sensory signal detection are not well known. Human and animal studies have reported that performance on signal detection tasks is highly state-dependent, exhibiting an inverted-U dependence on arousal and the activity of neuromodulatory pathways. This relationship, known as the Yerkes-Dodson curve, predicts that optimal performance occurs at intermediate levels of arousal (Aston-Jones and Cohen, 2005Aston-Jones G. Cohen J.D. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance.Annu. Rev. Neurosci. 2005; 28: 403-450Crossref PubMed Scopus (2607) Google Scholar; Cools and D’Esposito, 2011Cools R. D’Esposito M. Inverted-U-shaped dopamine actions on human working memory and cognitive control.Biol. Psychiatry. 2011; 69: e113-e125Abstract Full Text Full Text PDF PubMed Scopus (1077) Google Scholar, Murphy et al., 2011Murphy P.R. Robertson I.H. Balsters J.H. O’connell R.G. Pupillometry and P3 index the locus coeruleus-noradrenergic arousal function in humans.Psychophysiology. 2011; 48: 1532-1543Crossref PubMed Scopus (286) Google Scholar, Rajagovindan and Ding, 2011Rajagovindan R. Ding M. From prestimulus alpha oscillation to visual-evoked response: an inverted-U function and its attentional modulation.J. Cogn. Neurosci. 2011; 23: 1379-1394Crossref PubMed Scopus (95) Google Scholar, Vijayraghavan et al., 2007Vijayraghavan S. Wang M. Birnbaum S.G. Williams G.V. Arnsten A.F. Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory.Nat. Neurosci. 2007; 10: 376-384Crossref PubMed Scopus (751) Google Scholar, Yerkes and Dodson, 1908Yerkes R.M. Dodson J.D. The relation of strength of stimlus to rapidity of habit-formation.J. Comp. Neurol. Psychol. 1908; 18: 459-482Crossref Google Scholar). But what are the synaptic and circuit mechanisms of this inverted-U dependence of optimal states for behavior and neural responses? Reports on the cortical membrane potential dynamics associated with wakeful states have come to widely differing conclusion between species and sensory systems. Wakefulness is traditionally associated with low amplitude and arrhythmic scalp EEG. This canonical wakeful state has been referred to as the “activated” or “asynchronous” state, because excitatory neurons are thought to be tonically depolarized while interacting in a manner that reduces broad-ranging synchrony (Renart et al., 2010Renart A. de la Rocha J. Bartho P. Hollender L. Parga N. Reyes A. Harris K.D. The asynchronous state in cortical circuits.Science. 2010; 327: 587-590Crossref PubMed Scopus (685) Google Scholar, Steriade et al., 2001Steriade M. Timofeev I. Grenier F. Natural waking and sleep states: a view from inside neocortical neurons.J. Neurophysiol. 2001; 85: 1969-1985PubMed Google Scholar, van Vreeswijk and Sompolinsky, 1996van Vreeswijk C. Sompolinsky H. Chaos in neuronal networks with balanced excitatory and inhibitory activity.Science. 1996; 274: 1724-1726Crossref PubMed Scopus (1126) Google Scholar). Membrane potential recordings in awake cats support this view (Steriade et al., 2001Steriade M. Timofeev I. Grenier F. Natural waking and sleep states: a view from inside neocortical neurons.J. Neurophysiol. 2001; 85: 1969-1985PubMed Google Scholar), but recent work in rodent sensory cortical areas has revealed additional complexity. Membrane potentials in auditory cortex of rats have been found to be hyperpolarized and inactive across un-anesthetized states, though the arousal level of the animals was not quantified (Hromádka et al., 2008Hromádka T. Deweese M.R. Zador A.M. Sparse representation of sounds in the unanesthetized auditory cortex.PLoS Biol. 2008; 6: e16Crossref PubMed Scopus (373) Google Scholar, Hromádka et al., 2013Hromádka T. Zador A.M. DeWeese M.R. Up states are rare in awake auditory cortex.J. Neurophysiol. 2013; 109: 1989-1995Crossref PubMed Scopus (29) Google Scholar). Movement (walking or whisking) in mice is associated with cortical membrane potential dynamics akin to the “asynchronous” state in somatosensory, visual, and auditory cortical areas (Bennett et al., 2013Bennett C. Arroyo S. Hestrin S. Subthreshold mechanisms underlying state-dependent modulation of visual responses.Neuron. 2013; 80: 350-357Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar, Crochet and Petersen, 2006Crochet S. Petersen C.C. Correlating whisker behavior with membrane potential in barrel cortex of awake mice.Nat. Neurosci. 2006; 9: 608-610Crossref PubMed Scopus (395) Google Scholar, McGinley et al., 2013McGinley, M.J., David, S.V., and McCormick, D.A. (2013). Modulation of spontaneous and sensory-evoked synaptic dynamics in A1 during auditory discrimination tasks in mice. In Society for Neuroscience annual meeting.Google Scholar, Polack et al., 2013Polack P.O. Friedman J. Golshani P. Cellular mechanisms of brain state-dependent gain modulation in visual cortex.Nat. Neurosci. 2013; 16: 1331-1339Crossref PubMed Scopus (370) Google Scholar, Poulet and Petersen, 2008Poulet J.F. Petersen C.C. Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice.Nature. 2008; 454: 881-885Crossref PubMed Scopus (570) Google Scholar, Schneider et al., 2014Schneider D.M. Nelson A. Mooney R. A synaptic and circuit basis for corollary discharge in the auditory cortex.Nature. 2014; 513: 189-194Crossref PubMed Scopus (299) Google Scholar, Zhou et al., 2014Zhou M. Liang F. Xiong X.R. Li L. Li H. Xiao Z. Tao H.W. Zhang L.I. Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex.Nat. Neurosci. 2014; 17: 841-850Crossref PubMed Scopus (198) Google Scholar). However, while walking is associated with increases in stimulus-driven firing in the visual cortex (Bennett et al., 2013Bennett C. Arroyo S. Hestrin S. Subthreshold mechanisms underlying state-dependent modulation of visual responses.Neuron. 2013; 80: 350-357Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar, Niell and Stryker, 2010Niell C.M. Stryker M.P. Modulation of visual responses by behavioral state in mouse visual cortex.Neuron. 2010; 65: 472-479Abstract Full Text Full Text PDF PubMed Scopus (813) Google Scholar, Polack et al., 2013Polack P.O. Friedman J. Golshani P. Cellular mechanisms of brain state-dependent gain modulation in visual cortex.Nat. Neurosci. 2013; 16: 1331-1339Crossref PubMed Scopus (370) Google Scholar), it is associated with decreases in sensory-evoked responses in the auditory cortex (McGinley et al., 2013McGinley, M.J., David, S.V., and McCormick, D.A. (2013). Modulation of spontaneous and sensory-evoked synaptic dynamics in A1 during auditory discrimination tasks in mice. In Society for Neuroscience annual meeting.Google Scholar, Schneider et al., 2014Schneider D.M. Nelson A. Mooney R. A synaptic and circuit basis for corollary discharge in the auditory cortex.Nature. 2014; 513: 189-194Crossref PubMed Scopus (299) Google Scholar, Williamson et al., 2015Williamson R.S. Hancock K.E. Shinn-Cunningham B.G. Polley D.B. Locomotion and task demands differentially modulate thalamic audiovisual processing during active search.Current Biology. 2015; 26 (in press)Google Scholar, Zhou et al., 2014Zhou M. Liang F. Xiong X.R. Li L. Li H. Xiao Z. Tao H.W. Zhang L.I. Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex.Nat. Neurosci. 2014; 17: 841-850Crossref PubMed Scopus (198) Google Scholar). During stillness, slow (2–10 Hz) fluctuations are prominent in the somatosensory cortex (Crochet and Petersen, 2006Crochet S. Petersen C.C. Correlating whisker behavior with membrane potential in barrel cortex of awake mice.Nat. Neurosci. 2006; 9: 608-610Crossref PubMed Scopus (395) Google Scholar, Poulet and Petersen, 2008Poulet J.F. Petersen C.C. Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice.Nature. 2008; 454: 881-885Crossref PubMed Scopus (570) Google Scholar, Zagha et al., 2013Zagha E. Casale A.E. Sachdev R.N. McGinley M.J. McCormick D.A. Motor cortex feedback influences sensory processing by modulating network state.Neuron. 2013; 79: 567-578Abstract Full Text Full Text PDF PubMed Scopus (173) Google Scholar), are of intermittent prominence in the visual cortex (Bennett et al., 2013Bennett C. Arroyo S. Hestrin S. Subthreshold mechanisms underlying state-dependent modulation of visual responses.Neuron. 2013; 80: 350-357Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar, Polack et al., 2013Polack P.O. Friedman J. Golshani P. Cellular mechanisms of brain state-dependent gain modulation in visual cortex.Nat. Neurosci. 2013; 16: 1331-1339Crossref PubMed Scopus (370) Google Scholar, Reimer et al., 2014Reimer J. Froudarakis E. Cadwell C.R. Yatsenko D. Denfield G.H. Tolias A.S. Pupil fluctuations track fast switching of cortical states during quiet wakefulness.Neuron. 2014; 84: 355-362Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar), and variably reported in the auditory cortex (Hromádka et al., 2008Hromádka T. Deweese M.R. Zador A.M. Sparse representation of sounds in the unanesthetized auditory cortex.PLoS Biol. 2008; 6: e16Crossref PubMed Scopus (373) Google Scholar, Hromádka et al., 2013Hromádka T. Zador A.M. DeWeese M.R. Up states are rare in awake auditory cortex.J. Neurophysiol. 2013; 109: 1989-1995Crossref PubMed Scopus (29) Google Scholar, Schneider et al., 2014Schneider D.M. Nelson A. Mooney R. A synaptic and circuit basis for corollary discharge in the auditory cortex.Nature. 2014; 513: 189-194Crossref PubMed Scopus (299) Google Scholar, Zhou et al., 2014Zhou M. Liang F. Xiong X.R. Li L. Li H. Xiao Z. Tao H.W. Zhang L.I. Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex.Nat. Neurosci. 2014; 17: 841-850Crossref PubMed Scopus (198) Google Scholar). One possible explanation of these apparently divergent results is that state (arousal) fluctuates continuously and rapidly, resulting in a high degree of variability between studies and between moments within individual studies, which has not been accounted for. The diameter of the pupil and the occurrence of sharp-wave activity in the hippocampus are two well-established measures of arousal (Bradley et al., 2008Bradley M.M. Miccoli L. Escrig M.A. Lang P.J. The pupil as a measure of emotional arousal and autonomic activation.Psychophysiology. 2008; 45: 602-607Crossref PubMed Scopus (1127) Google Scholar, Buzsáki, 1986Buzsáki G. Hippocampal sharp waves: their origin and significance.Brain Res. 1986; 398: 242-252Crossref PubMed Scopus (636) Google Scholar, Gilzenrat et al., 2010Gilzenrat M.S. Nieuwenhuis S. Jepma M. Cohen J.D. Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function.Cogn. Affect. Behav. Neurosci. 2010; 10: 252-269Crossref PubMed Scopus (483) Google Scholar, Laeng et al., 2012Laeng B. Sirois S. Gredeback G. Pupillometry: A window to the preconscious?.Perspect. Psychol. Sci. 2012; 7: 18-27Crossref PubMed Scopus (553) Google Scholar, Reimer et al., 2014Reimer J. Froudarakis E. Cadwell C.R. Yatsenko D. Denfield G.H. Tolias A.S. Pupil fluctuations track fast switching of cortical states during quiet wakefulness.Neuron. 2014; 84: 355-362Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar, Siegle et al., 2003Siegle G.J. Steinhauer S.R. Stenger V.A. Konecky R. Carter C.S. Use of concurrent pupil dilation assessment to inform interpretation and analysis of fMRI data.Neuroimage. 2003; 20: 114-124Crossref PubMed Scopus (164) Google Scholar, Vinck et al., 2015Vinck M. Batista-Brito R. Knoblich U. Cardin J.A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding.Neuron. 2015; 86: 740-754Abstract Full Text Full Text PDF PubMed Scopus (378) Google Scholar). Indeed, in rodents, measures of the pupil diameter have been used for binary classification of “aroused” or “unaroused” states and predict differences in sensory response magnitude and reliability and the extent of slow fluctuations in visual cortical membrane potential (Reimer et al., 2014Reimer J. Froudarakis E. Cadwell C.R. Yatsenko D. Denfield G.H. Tolias A.S. Pupil fluctuations track fast switching of cortical states during quiet wakefulness.Neuron. 2014; 84: 355-362Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar). We wondered if pupil diameter and hippocampal sharp-wave activity could be used to derive a continuous measure of arousal level, allowing study of the membrane potential dynamics associated with the inverted-U shaped dependence of signal detection task performance on arousal. Here we report that the mouse’s internal state fluctuates continuously and rapidly (in seconds or less), and arousal can be quantified simply as the diameter of the pupil. We find that pupil diameter closely tracks the rate of occurrence of hippocampal sharp waves. In addition, auditory cortical membrane potentials of layer 4 and 5 excitatory neurons exhibit: slowly fluctuating (1–10 Hz) rhythmic activity with low arousal, hyperpolarization and low variability at intermediate arousal, depolarization and variability with sustained hyper-arousal (with or without walking), and transient depolarization in synchrony with micro-arousal events. These results provide a framework with which to resolve apparent discrepancies between species and sensory systems in cortical membrane potential dynamics and reveal the membrane potential signature of optimal performance in a signal detection task. Substantial portions of this work have been presented previously in abstract form (McGinley et al., 2013McGinley, M.J., David, S.V., and McCormick, D.A. (2013). Modulation of spontaneous and sensory-evoked synaptic dynamics in A1 during auditory discrimination tasks in mice. In Society for Neuroscience annual meeting.Google Scholar, McGinley et al., 2014McGinley, M.J., David, S.V., and McCormick, D.A. (2014). Predicting cortical response variability in awake-behaving mice: the role of arousal, behavioral performance, and auditory stimuli. Association for Research in Otolaryngology abstracts.Google Scholar). We hypothesized that the arousal state of waking mice exhibits rapid moment-to-moment variations that impact behavior and cortical activity. We further hypothesized that there is an “optimal” state in which the behavioral performance of the animal on a task would peak and sensory responses would be large and reliable. Finally, we sought to determine the dynamic signature of membrane potential activity in the optimal sensory signal detection state. To this end, we trained mice in a challenging auditory tone-in-noise detection task and quantified the state of the mice in several conditions: (1) in the absence of sound exposure, (2) during passive listening to task stimuli (temporally orthogonal ripple combinations [TORCs]) (Atiani et al., 2009Atiani S. Elhilali M. David S.V. Fritz J.B. Shamma S.A. Task difficulty and performance induce diverse adaptive patterns in gain and shape of primary auditory cortical receptive fields.Neuron. 2009; 61: 467-480Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar)), or (3) during the performance of an auditory task detecting tones embedded in these TORCs (see Experimental Procedures). To quantify state, we used pupillometry, rate of occurrence of sharp wave/ripple combinations and theta activity (θ/δ ratio; see Csicsvari et al., 1999Csicsvari J. Hirase H. Czurkó A. Mamiya A. Buzsáki G. Oscillatory coupling of hippocampal pyramidal cells and interneurons in the behaving Rat.J. Neurosci. 1999; 19: 274-287PubMed Google Scholar) in the CA1 field of the hippocampus and spontaneous locomotor patterns on a cylindrical treadmill (Figures 1, S1, and S2). These measures relate to the internal state of the animal (Buzsáki, 1986Buzsáki G. Hippocampal sharp waves: their origin and significance.Brain Res. 1986; 398: 242-252Crossref PubMed Scopus (636) Google Scholar, Loewenfeld, 1999Loewenfeld I.E. The Pupil. Anatomy, Physiology, and Clinical Applications. Volume 1. Butterworth Heinemann, Boston1999Google Scholar, Reimer et al., 2014Reimer J. Froudarakis E. Cadwell C.R. Yatsenko D. Denfield G.H. Tolias A.S. Pupil fluctuations track fast switching of cortical states during quiet wakefulness.Neuron. 2014; 84: 355-362Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar). Pupil diameter in waking mice varied widely on multiple time scales over the 1–3 hr recording/behavioral sessions (N = 41; n = 83), ranging from nearly pinpoint (∼0.15 mm) to nearly fully dilated (∼1.8 mm) in constant low-light conditions (see e.g., Figures 1B, 1C, 2A, 2B, 3A, 6A, 6C, S2, S4, and S5). Pupil dilations exhibited a broad range of periodicities and amplitudes, but three features were prominent: (1) frequent and brief (∼2 to 3 s duration) dilations (Figures 1C and 2), (2) slower constrictions over periods of 5–30 s (Figures 1C and 3), and (3) nearly fully dilated pupils encompassing periods of walking (Figures 1B and 2A). We term the rapid, relatively brief dilations, “microdilations” to differentiate them from more prolonged changes in diameter. We detected microdilations by their rapid rate of rise of pupil diameter at their onset (see Experimental Procedures; Figures 1C and S4D). To determine the relationship between the pupil diameter and brain state, we compared the moment-to-moment pupil diameter to fast ripples associated with sharp waves and theta activity in the hippocampus during spontaneous state changes in the absence of sensory stimulation (see Experimental Procedures). The pupil diameter exhibited a strong inverse relationship to the rate of occurrence of fast ripples (Figures 1C–1F and S1), with a peak coherence of 0.78 ± 0.07 (n = 6 cells from N = 6 animals) at low frequencies (<0.05 Hz) and a significant coherence at all frequencies up to 1 Hz (Figures 1E). Theta activity was largely associated with walking (Figures 1C, S1B, and S1C). These results support the view that pupil diameter may be a useful indicator of brain state (Murphy et al., 2011Murphy P.R. Robertson I.H. Balsters J.H. O’connell R.G. Pupillometry and P3 index the locus coeruleus-noradrenergic arousal function in humans.Psychophysiology. 2011; 48: 1532-1543Crossref PubMed Scopus (286) Google Scholar, Reimer et al., 2014Reimer J. Froudarakis E. Cadwell C.R. Yatsenko D. Denfield G.H. Tolias A.S. Pupil fluctuations track fast switching of cortical states during quiet wakefulness.Neuron. 2014; 84: 355-362Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar, Vinck et al., 2015Vinck M. Batista-Brito R. Knoblich U. Cardin J.A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding.Neuron. 2015; 86: 740-754Abstract Full Text Full Text PDF PubMed Scopus (378) Google Scholar). To ensure that pupil dilations were not in response to changes in illumination due to eye movements, we also measured eyelid opening in darkness (Figure S2), since eyelid opening and pupil diameter are coupled together through co-innervation by the sympathetic nervous system (Loewenfeld, 1999Loewenfeld I.E. The Pupil. Anatomy, Physiology, and Clinical Applications. Volume 1. Butterworth Heinemann, Boston1999Google Scholar). As expected, pupil dilation and eyelid opening fluctuated coherently, such that even in darkness (where pupil dilation is saturated) eyelid opening exhibited the same slow changes associated with walking or disengagement and micro-openings that are characteristic of pupil diameter (Figure S2; N = 7 animals). We also found that an easily measured proxy for changes in lid opening and pupil diameter is the amount of light reflected from the eye from an infrared light source, a measure we term eye-indexed state (EIS) (Figures 2A, 2B, and S2; N = 7). These results indicate that pupil dilation in constant illumination, eyelid opening in the dark, or EIS in either condition are precise, accurate, and easily obtained measures of brain state. Large pupillary dilations, whisking, walking, and other movements frequently, but not always, occurred simultaneously as part of a larger realm of behavioral state changes, as observed previously (Mitchinson et al., 2011Mitchinson B. Grant R.A. Arkley K. Rankov V. Perkon I. Prescott T.J. Active vibrissal sensing in rodents and marsupials.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2011; 366: 3037-3048Crossref PubMed Scopus (89) Google Scholar, Reimer et al., 2014Reimer J. Froudarakis E. Cadwell C.R. Yatsenko D. Denfield G.H. Tolias A.S. Pupil fluctuations track fast switching of cortical states during quiet wakefulness.Neuron. 2014; 84: 355-362Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar, Schneider et al., 2014Schneider D.M. Nelson A. Mooney R. A synaptic and circuit basis for corollary discharge in the auditory cortex.Nature. 2014; 513: 189-194Crossref PubMed Scopus (299) Google Scholar). We primarily focus on pupil diameter as a measure of state, owing to its ease of measurement, non-invasive nature, and ability to continuously and sensitively track state changes, whether or not they accompany overt walking, whisking, or other movements (see e.g., Movie S1). The maximal pupil diameter achieved during each recording or behavioral session always occurred during walking and varied, slightly, from day to day and animal to animal. Thus, to facilitate population-level comparisons and analysis, we normalized the pupil diameter by dividing by its maximal value in each session (see Figure 1B; see Experimental Procedures). Next we examined if pupil diameter/eyelid opening was related to the state of activity in auditory cortical neurons. We performed whole-cell recordings from auditory cortex (ACtx) layer 4 or 5 presumed excitatory neurons (see Figure S3) in waking animals, measured spontaneous activity without sound presentation, and measured either pupil diameter (n = 9 neurons in N = 7 animals) or EIS (n = 7 neurons in N = 5 animals). Remarkably, the microdilations/micro-openings observed in still animals were reliably associated with 5–20 mV time-locked depolarization of cortical neuronal membrane potential (Figure 2; see also Figures 3 and S4; n = 16 neurons). These transient depolarizations preceded pupil dilation/eyelid opening by 1.1 ± 0.1 s (n = 16; see Figure 2C, inset). Much or all of this lag may represent the slow time course of pupillary/eyelid movement to alterations in sympathetic and parasympathetic activity (Loewenfeld, 1999Loewenfeld I.E. The Pupil. Anatomy, Physiology, and Clinical Applications. Volume 1. Butterworth Heinemann, Boston1999Google Scholar). Microdilations were of a similar duration as the transient depolarizations (Figures 2A and 2B), and typically exhibited a rapid rise and a slower return to baseline (see e.g., Figures 2A, 2B, 3A, and S4D), suggestive of activation of the sympathetic innervation of pupillary dilator muscles (Loewenfeld, 1999Loewenfeld I.E. The Pupil. Anatomy, Physiology, and Clinical Applications. Volume 1. Butterworth Heinemann, Boston1999Google Scholar). We performed detailed analysis of the nine recordings with long, stable membrane potential recordings, high-quality pupillometry data, and in which the pupil of the animal exhibited variation over a broad range of diameters. Membrane potential of cortical neurons exhibited a remarkably high coherence (calculated in a 200 s sliding window; see Experimental Procedures) with pupil diameter, peaking at 0.7 ± 0.1 at approximately 0.03 Hz (Figure 2C; n = 9). The close relationship between microdilations and transient depolarization was apparent as a peak in coherence at ∼0.3 Hz, reflecting their seconds-long time course (Figure 2C). In addition to a high correlation between cortical membrane potential and pupil diameter during microdilations, we also noted that in anticipation of a bout of walking, cortical membrane potential depolarized, followed (∼1 s later) by large dilation of the pupil and finally (3 to 4 s later) walking (Figures 2 and S4). Walking was always associated with large diameter pupils (Figure 1B; see also Figure 6C), indicating that our animals only walked during high arousal states, and that the transition from still to walking is typically associated with a large change in arousal. Following the cessation of walking, the pupil diameter slowly constricted and cortical membrane potential slowly repolarized (n = 9; Figures 2 and S4). The changes in membrane potential we observed in cortical neurons with pupil dilations or walking did not result from disruptions of recording quality, since the membrane potential depolarization initiated prior to each behavioral event (e.g., dilation or walking) and always returned to pre-movement levels once movement or arousal ceased. Furthermore, spike threshold was only weakly correlated (r = −0.16 ± 0.08) with pupil diameter and varied largely in relation to the rate-of-rise of the membrane potential leading up to action potential threshold, irrespective of pupillary or walking patterns (Figure S3H–S3J; see Experimental Procedures) (Azouz and Gray, 2000Azouz R. Gray C.M. Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo.Proc. Natl. Acad. Sci. USA. 2000; 97: 8110-8115Crossref PubMed Scopus (369) Google Scholar, McGinley and Oertel, 2006McGinley M.J. Oertel D. Rate thresholds determine the precision of temporal integration in principal cells of the ventral cochlear nucleus.Hear. Res. 2006; 216-217: 52-63Crossref PubMed Scopus (76) Google Scholar). In addition to the high positive correlation between pupil diameter/eyelid microdilations and cortical neuronal membrane potential (e.g., Figures 2A and S4) that occurred during periods of intermediate to high arousal, additional features were apparent. Constricted pupil diameters (e.g., low arousal) were associated with slow rhythmic synaptic activity, resulting in high power in the low (2–10 Hz) and delta (1–4 Hz) frequency ranges, resulting in high membrane potential variance (see e.g., Figures 2A, 2B, 3A, S5, and S6). Dilation of the pupils from small toward intermediate diameters resulted in a strong suppression of this low-frequency synaptic activity, which in turn resulted in an average hyperpolarization of t" @default.
• W1519578210 created "2016-06-24" @default.
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• W1519578210 date "2015-07-01" @default.
• W1519578210 modified "2023-10-18" @default.
• W1519578210 title "Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection" @default.
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