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Review
. 2015 Sep 23;87(6):1143-1161.
doi: 10.1016/j.neuron.2015.09.012.

Waking State: Rapid Variations Modulate Neural and Behavioral Responses

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Free PMC article
Review

Waking State: Rapid Variations Modulate Neural and Behavioral Responses

Matthew J McGinley et al. Neuron. .
Free PMC article

Abstract

The state of the brain and body constantly varies on rapid and slow timescales. These variations contribute to the apparent noisiness of sensory responses at both the neural and the 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.

Figures

Figure 1
Figure 1
Characteristics of state change in cortical networks. A. Simultaneous intracellular recording from a cortical pyramidal cell and extracellular cortical field potential in the transition from slow wave sleep to waking. During slow wave sleep, slow waves are prominent in the local field potential and appear as an alternation between depolarized, active, Up states, and hyperpolarized, inactive, Down states. The transition to waking is associated with a suppression of slow rhythms in the local field potential and the loss of Down states, resulting in the persistent depolarization of the pyramidal neuron. B. Whole cell recording from a fast spiking interneuron in the primary visual cortex of an awake mouse reveals that movement (locomotion) is associated with depolarization of the membrane potential and suppression of low frequency fluctuations in synaptic activity. C. Characterization of behavioral state in rodents by principle component analysis of the activity of multiple brain areas reveals the major sleep-waking states seen behaviorally. Note that although the states exist within their own portions of state-space, they are not completely distinct and separate (left). Movement between states follows repeated paths (right). Abbreviations: AE, active exploration; IS, intermediate stage; REM, rapid eye movement sleep; SWS, slow wave sleep; QW, quiet wake; WT, whisker twitching. A from (Steriade et al., 2001); B from (Polack et al., 2013); C from (Gervasoni et al., 2004).
Figure 2
Figure 2
Pupil diameter is an accurate predictor of variations in multiple parameters related to brain state. A. Schematic diagram of experimental protocol. Whole cell recordings are obtained from a cortical neuron while simultaneously monitoring pupil diameter, locomotion on a cylindrical treadmill, and, in some cases, hippocampal local field potential. B. Simultaneous recording of pupil diameter and cortical neuron membrane potential in layer 2/3 of the mouse primary visual cortex. Pupil diameter exhibits spontaneous variations in size even in the “quiet awake” state and in the absence of overt locomotion. Note the strong relationship between slow (2–10 Hz) rhythmic synaptic activity and constriction and the suppression of this activity with dilation (labeled ‘desync’). C. Comparison of pupil diameter and the density of low frequency (2–10 Hz) rhythmic synaptic activity (up indicates decreased 2–10 Hz power) in an auditory cortical layer 5 pyramidal cell. Note the tight anti-correlation between these two variables, with increases in pupil diameter being associated with prominent suppression of low frequency synaptic activity. D. Comparison of pupil diameter and the rate of occurrence of ripples in the CA1 field of the hippocampus. Note the tight anti-correlation between ripple rate (up indicates decreased ripples) and pupil diameter. Increases in pupil diameter are associated with suppression of ripples in the hippocampus. B from (Reimer et al., 2014); C, D from (McGinley et al., 2015).
Figure 3
Figure 3
Waking state rapidly varies between multiple levels of arousal. A. Whole cell recoding from a layer 5 pyramidal neuron in auditory cortex of an awake mouse. No sound was being presented. The second trace is eye-indexed state (EIS; a proxy for pupil diameter determined by measuring reflected infrared light from the exposed surface of the eye) (McGinley et al., 2015). The bottom trace is locomotion velocity on a treadmill. At least 4 waking sub-states can be readily identified: 1) quiescence associated with small pupil diameter and prominent low frequency rhythmic synaptic activity (expanded in B1); 2) brief dilations of the pupil, termed microdilations, are highlighted in gray and are associated with a suppression of the low frequency activity and a depolarization of this neuron; 3) locomotion, associated with a strong suppression of lower frequency synaptic activity, depolarization, pupil dilation, and an increase in higher frequency components of synaptic potential activity; 4) intermediate levels of arousal, as indicated by intermediate pupil diameter, suppression of lower frequency rhythmic activity, a hyperpolarized and relatively quiet membrane potential. Note that the animal spends only seconds within each state and that the level of arousal, as indicated by pupil diameter, rhythmic activity, and membrane potential, is constantly varying even though the animal is awake the entire period. B. Expansion of indicated portions of the trace in A for detail. C. Membrane potential of deep lying pyramidal neurons (n=9) exhibits a U-shaped relationship with pupil diameter. As pupil diameter increases from small (e.g. 20% dilated) to intermediate (e.g. 50–60% dilated), slow fluctuations in synaptic activity (e.g. state 1) are suppressed and therefore the membrane potential is more hyperpolarized and variance is decreased (e.g. state 4 above). Further increases in pupil dilation (e.g. > 60%), such as with locomotion, result in an average depolarization and the increased appearance of barrages of synaptic activity (e.g. states 2 or 3 above). Scale bar is % of time at that membrane potential for that pupil diameter bin. From (McGinley et al., 2015).
Figure 4
Figure 4
Locomotion and arousal strongly modulate multiple properties of cortical activity. A. Example of the effects of locomotion on mouse visual cortical activity. Video frame images of the mouse’s eye (1–6) are shown and where acquired at the times indicated in the pupil recording trace. Pupil diameter was recorded on video and extracted post hoc via a fitted ellipse (cyan). The average pupil diameter in pixel units is shown as a function of time. Locomotion is shown as a linearized version of the wheel position. Locomotion onset point is shown in the inset. The locomotion period is indicated by green shading. Local field potential (LFP) recording from layer 2/3 of the primary visual cortex is shown as a raw broadband LFP signal, together with the 1–4 Hz filtered signals. Thresholded multi-unit traces and spike densities (1 s Gaussian smoothing kernel with SD of 0.25 s) are shown for a layer 2/3 multiple unit recording. Grey shadings indicate visual stimuli at 100% contrast and varying orientations. B. Increasing arousal with a puff of air delivered to the back of a quiescent mouse results in suppression of low (1–4 Hz) frequency rhythms and enhancement of higher (55–65 Hz) rhythms in the LFP, together with a significant increase in pupil diameter in the absence of locomotion. Note that the alterations in cortical power track changes in pupil diameter following the arousing stimulus. From (Vinck et al., 2015).
Figure 5
Figure 5
Alterations in visual responses by state and locomotion. A. Raster plots of the visual responses of an example layer 2/3 pyramidal cell with associated firing rate density (computed using ± 0.025 s Gaussian kernels with SD of 0.0125 s) during locomotion, quiet awake early (3–20 s after locomotion offset) and quiet awake, later (>40 s after locomotion offset). Gray shading and sinusoid indicate visual stimulation. B. Orientation tuning is enhanced during pupil dilation. B, left. Mean fluorescence image colored by orientation preferences of individual pixels; scale bar, 50 µm. B, middle. Average tuning curves aligned to cells’ preferred direction for active (running and/or whisking) periods (green) and quiet (black) periods. Peak responses are increased (20%, p < 10−12) and orientation selectivity is unchanged (7% decrease, p = 0.07). Error bands are SEM computed over cells (n = 516). B, right. Average tuning during pupil dilation (red) and constriction (blue) during quiet periods (excluding running and whisking). In contrast with the effects of locomotion, orientation selectivity is significantly increased during dilation compared to constriction (16% increase in mean OSI, p < 10−6). Cells also respond more reliably during dilation compared to constriction (28% increase in mean binned R2 values of stimulus responses of individual cells, p < 10−15). A from (Vinck et al., 2015); B from (Reimer et al., 2014).
Figure 6
Figure 6
Schematic illustration of the effects of locomotion and arousal on cortical neuronal activities and responses. A. Increases in pupil dilation in the absence of overt locomotion result in decreased low (2–10 Hz) frequency power, decreased correlations between activity in neighboring neurons, and decreased variability of sensory evoked responses. In addition, pupillary dilation is associated with an increase in signal-to-noise (evoked/spontaneous) ratio, reliability of visually evoked responses and an increase in power in the gamma band. B. Locomotion is also associated with marked decreases in 2–10 Hz power, noise correlations, and variability of visually evoked responses. Locomotion is also associated with an enhancement of S/N ratio and reliability of visually evoked responses, in comparison to the average non-locomotion state, and an increase in power in the gamma frequencies. Locomotion also has effects that differ from arousal without locomotion (see text). (Figure to be redrawn by Neuron Artist.)
Figure 7
Figure 7
Optimal performance on an auditory detection task occurs at intermediate levels of arousal. A. Mice spontaneously walked or sat quietly while performing the auditory detection task. Locomotion did not affect reward or trial structure. B. Animals were presented with 1 second periods of complex sounds (temporally orthogonal ripple combinations), in which was occasionally placed a pure tone. If the animal licked during the presentation of the tone, a Hit was recorded and the animal received a liquid reward. If the animal licked when no tone was present, a false alarm (FA) was recorded, while if the animal missed the presence of the tone, a Miss was recorded. C. Performance on the detection task varies with pupil diameter (non-locomotion periods). Hit rate (green) peaked at intermediate pupil diameters, while lick latency (red) was shortest, also at intermediate pupil diameters. Small or large pupil diameters, indicating low or high arousal levels, were associated with non-optimal task performance. Tone level in this example was 35 dB below average sound level of the complex sound, making it a challenging detection task. Responses obtained during walking (locomotion) are illustrated separately from those obtained during stillness. Walking had an effect similar to the high aroused, non-walking state, with a decrease in performance and an increase in lick latency. D. Cortical gain peaks at intermediate levels of arousal (pupil diameter). Walking is associated with a marked decrease in cortical gain in auditory cortex. The optimal state for sensory evoked responses and behavioral performance corresponds to a hyperpolarized average membrane potential in layer 5 auditory cortical neurons. From (McGinley et al., 2015).
Figure 8
Figure 8
Alternative idealized relationships between arousal and cortical membrane potential and oscillations. A. Binary model of arousal in which waking is divided into an “inactivated” state with prominent low frequency oscillations and an “activated” state with enhanced higher frequency activities. B. Sigmoidal model of arousal based upon intracellular recordings in the transition from slow wave sleep to waking (Figure 1A) or from quiet waking to movement in somatosensory and visual cortex (Figure 1B). The transition from low to medium arousal (b) is associated with a suppression of low (2–10 Hz) frequency rhythms and a depolarization of some, but not all, cortical neurons. The transition to high arousal (c), particularly with movement, results in a strong enhancement of activity in the gamma frequency band and further depolarization of some neurons. C. The U-shaped model of arousal contrasts with the sigmoid model in that the membrane potential of depolarization-activated cortical neurons exhibits a low point in between low and high arousal. Gamma is drawn as increasing from intermediate to low arousal owing to the increased activation of cortical networks by slow oscillations. These idealized models are only for comparison and it is expected that real cortical networks will operate in a regime that may mix different features of all three models.
Figure 9
Figure 9
Schematic of proposed mechanisms for state dependent modulation of cortical activity and responsiveness. A. Schematic diagram illustrating the major neural circuits involved in control of brain state and pupil diameter. B. Schematic circuit components illustrating the potentially important roles for disinhibition and modulation in the control of neuronal responses. The release of ACh by basal forebrain (BF) neurons may activate layer 2/3 VIP interneurons through nicotinic receptors. Activation of these cells may decrease the excitability of dendritic targeting interneurons (e.g. somatostatin – SOM – interneurons) or the activity of other types of interneurons (e.g. PV expressing interneurons), resulting in an increase in excitability of pyramidal cells. Alternatively, activity in multiple pathways, including neuromodulators from the locus coeruleus and basal forebrain, corticocortical connections from the frontoparietal (e.g. motor) cortex, or excitatory inputs from the thalamus may modulate multiple components of the cortical circuit. Through as of yet unknown mechanisms, the state of the cortex is coupled together with the state of the peripheral nervous system, resulting in a high correlation between cortical state and pupil diameter. One possibility is that the activity of the locus coeruleus is intimately involved in both. C. Time-course of cholinergic fiber activity observed with 2-photon monitoring of GCamp6 labeled cholinergic fibers in the superficial layers of the somatosensory cortex. Note that whisker movement is associated with large changes in cholinergic axonal activity. D. Vm (black) and quantified whisker movement (green) during control period and during blue light illumination to stimulate basal forebrain cholinergic neurons expressing ChR2 (ChAT ChR2) in a thalamus-inactivated mouse. Abbreviations: BF – Basal Forebrain; CG – Ciliary Ganglion; EW - Edinger-Westphal nucleus; FPCtx – Frontal-Parietal Cortex; LC - Locus Coeruleus; IML – Intralaminar neurons of the spinal cord; PV – Parvalbumin containing interneurons; SCG – Superior Cervical Ganglion; SOM – somatostatin containing interneurons; VIP – vasoactive intestinal peptide containing interneurons; C and D from (Eggermann et al., 2014).

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