Local cortical desynchronization and pupil-linked arousal differentially shape brain states for optimal sensory performance

Beyond task response-Pre-stimulus activity modulates contents of consciousness

The current discussion on the neural correlates of the contents of consciousness (NCCc) focuses mainly on the post-stimulus period of task-related activity. This neglects the substantial impact of the spontaneous or ongoing activity of the brain as manifest in pre-stimulus activity. Does the interaction of pre- and post-stimulus activity shape the contents of consciousness? Addressing this gap in our knowledge, we review and converge two recent lines of findings, that is, pre-stimulus alpha power and pre- and post-stimulus alpha trial-to-trial variability (TTV). The data show that pre-stimulus alpha power modulates post-stimulus activity including specifically the subjective features of conscious contents like confidence and vividness. At the same time, alpha pre-stimulus variability shapes post-stimulus TTV reduction including the associated contents of consciousness. We propose that non-additive rather than merely additive interaction of the internal pre-stimulus activity with the external stimulus in the alpha band is key for contents to become conscious. This is mediated by mechanisms on different levels including neurophysiological, neurocomputational, neurodynamic, neuropsychological and neurophenomenal levels. Overall, considering the interplay of pre-stimulus intrinsic and post-stimulus extrinsic activity across wider timescales, not just evoked responses in the post-stimulus period, is critical for identifying neural correlates of consciousness. This is well in line with both processing and especially the Temporo-spatial theory of consciousness (TTC).

Pharmacological Elevation of Catecholamine Levels Improves Perceptual Decisions, But Not Metacognitive Insight

Perceptual decisions are often accompanied by a feeling of decision confidence. Where the parietal cortex is known for its crucial role in shaping such perceptual decisions, metacognitive evaluations are thought to additionally rely on the (pre)frontal cortex. Because of this supposed neural differentiation between these processes, perceptual and metacognitive decisions may be divergently affected by changes in internal (e.g., attention, arousal) and external (e.g., task and environmental demands) factors. Although intriguing, causal evidence for this hypothesis remains scarce. Here, we investigated the causal effect of two neuromodulatory systems on behavioral and neural measures of perceptual and metacognitive decision-making. Specifically, we pharmacologically elevated levels of catecholamines (with atomoxetine) and acetylcholine (with donepezil) in healthy adult human participants performing a visual discrimination task in which we gauged decision confidence, while electroencephalography was measured. Where cholinergic effects were not robust, catecholaminergic enhancement improved perceptual sensitivity, while at the same time leaving metacognitive sensitivity unaffected. Neurally, catecholaminergic elevation did not affect sensory representations of task-relevant visual stimuli but instead enhanced well-known decision signals measured over the centroparietal cortex, reflecting the accumulation of sensory evidence over time. Crucially, catecholaminergic enhancement concurrently impoverished neural markers measured over the frontal cortex linked to the formation of metacognitive evaluations. Enhanced catecholaminergic neuromodulation thus improves perceptual but not metacognitive decision-making.

On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time

Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet of control, impulsivity, and self-regulation. The stop signal task is argued to be the purest inhibition task we have, and it is thus central to much work investigating the role of inhibition in areas like development and psychopathology. Most of this work quantifies stopping behavior by calculating the stop signal reaction time as a measure of individual stopping latency. Individual difference studies aiming to investigate why and how stopping latencies differ between people often do this under the assumption that the stop signal reaction time indexes a stable, dispositional trait. However, empirical support for this assumption is lacking, as common measures of inhibition and control tend to show low test-retest reliability and thus appear unstable over time. The reasons for this could be methodological, where low stability is driven by measurement noise, or substantive, where low stability is driven by a larger influence of state-like and situational factors. To investigate this, we characterized the split-half and test-retest reliability of a range of common behavioral and electrophysiological measures derived from the stop signal task. Across three independent studies, different measurement modalities, and a systematic review of the literature, we found a pattern of low temporal stability for inhibition measures and higher stability for measures of manifest behavior and non-inhibitory processing. This pattern could not be explained by measurement noise and low internal consistency. Consequently, response inhibition appears to have mostly state-like and situational determinants, and there is little support for the validity of conceptualizing common inhibition measures as reflecting stable traits.

Double Dissociation of Spontaneous Alpha-Band Activity and Pupil-Linked Arousal on Additive and Multiplicative Perceptual Gain

Perception is a probabilistic process dependent on external stimulus properties and one's internal state. However, which internal states influence perception and via what mechanisms remain debated. We studied how spontaneous alpha-band activity (8-13 Hz) and pupil fluctuations impact visual detection and confidence across stimulus contrast levels (i.e., the contrast response function, CRF). In human subjects of both sexes, we found that low prestimulus alpha power induced an "additive" shift in the CRF, whereby stimuli were reported present more frequently at all contrast levels, including contrast of zero (i.e., false alarms). Conversely, prestimulus pupil size had a "multiplicative" effect on detection such that stimuli occurring during large pupil states (putatively corresponding to higher arousal) were perceived more frequently as contrast increased. Signal detection modeling reveals that alpha power changes detection criteria equally across the CRF but not detection sensitivity (d'), whereas pupil-linked arousal modulated sensitivity, particularly for higher contrasts. Interestingly, pupil size and alpha power were positively correlated, meaning that some of the effect of alpha on detection may be mediated by pupil fluctuations. However, pupil-independent alpha still induced an additive shift in the CRF corresponding to a criterion effect. Our data imply that low alpha boosts detection and confidence by an additive factor, rather than by a multiplicative scaling of contrast responses, a profile which captures the effect of pupil-linked arousal. We suggest that alpha power and arousal fluctuations have dissociable effects on behavior. Alpha reflects the baseline level of visual excitability, which can vary independent of arousal.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

Episodic long-term memory formation during slow-wave sleep

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words' linguistic processing raised neural complexity. The words' semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

Greater up-modulation of intra-individual brain signal variability makes a high-load cognitive task more arduous for older adults

The extent to which brain responses are less distinctive across varying cognitive loads in older adults is referred to as neural dedifferentiation. Moment-to-moment brain signal variability, an emerging indicator, reveals not only the adaptability of an individual's brain as an inter-individual trait, but also the allocation of neural resources within an individual due to ever-changing task demands, thus shedding novel insight into the process of neural dedifferentiation. However, how the modulation of intra-individual brain signal variability reflects behavioral differences related to cognitively demanding tasks remains unclear. In this study, we employed functional near-infrared spectroscopy (fNIRS) imaging to capture the variability of brain signals, which was quantified by the standard deviation, during both the resting state and an n-back task (n = 1, 2, 3) in 57 healthy older adults. Using multivariate Partial Least Squares (PLS) analysis, we found that fNIRS signal variability increased from the resting state to the task and increased with working memory load in older adults. We further confirmed that greater fNIRS signal variability generally supported faster and more stable response time in the 2- and 3-back conditions. However, the intra-individual level analysis showed that the greater the up-modulation in fNIRS signal variability with cognitive loads, the more its accuracy decreases and mean response time increases, suggesting that a greater intra-individual brain signal variability up-modulation may reflect decreased efficiency in neural information processing. Taken together, our findings offer new insights into the nature of brain signal variability, suggesting that inter- and intra-individual brain signal variability may index distinct theoretical constructs.

Modulation of metastable ensemble dynamics explains optimal coding at moderate arousal in auditory cortex

Performance during perceptual decision-making exhibits an inverted-U relationship with arousal, but the underlying network mechanisms remain unclear. Here, we recorded from auditory cortex (A1) of behaving mice during passive tone presentation, while tracking arousal via pupillometry. We found that tone discriminability in A1 ensembles was optimal at intermediate arousal, revealing a population-level neural correlate of the inverted-U relationship. We explained this arousal-dependent coding using a spiking network model with a clustered architecture. Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal). Additional signatures of this transition include arousal-induced reductions of overall neural variability and the extent of stimulus-induced variability quenching, which we observed in the empirical data. Altogether, this study elucidates computational principles underlying interactions between pupil-linked arousal, sensory processing, and neural variability, and suggests a role for phase transitions in explaining nonlinear modulations of cortical computations.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which in turn modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (N=149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

Assessing cortical excitability with electroencephalography: A pilot study with EEG-iTBS

BACKGROUND

Cortical excitability measures neural reactivity to stimuli, usually delivered via Transcranial Magnetic Stimulation (TMS). Excitation/inhibition balance (E/I) is the ongoing equilibrium between excitatory and inhibitory activity of neural circuits. According to some studies, E/I could be estimated in-vivo and non-invasively through the modeling of electroencephalography (EEG) signals and termed 'intrinsic excitability' measures. Several measures have been proposed (phase consistency in the gamma band, sample entropy, exponent of the power spectral density 1/f curve, E/I index extracted from detrend fluctuation analysis, and alpha power). Intermittent theta burst stimulation (iTBS) of the primary motor cortex (M1) is a non-invasive neuromodulation technique allowing controlled and focal enhancement of TMS cortical excitability and E/I of the stimulated hemisphere.

OBJECTIVE

Investigating to what extent E/I estimates scale with TMS excitability and how they relate to each other.

METHODS

M1 excitability (TMS) and several E/I estimates extracted from resting state EEG recordings were assessed before and after iTBS in a cohort of healthy subjects.

RESULTS

Enhancement of TMS M1 excitability, as measured through motor-evoked potentials (MEPs), and phase consistency of the cortex in high gamma band correlated with each other. Other measures of E/I showed some expected results, but no correlation with TMS excitability measures or strong consistency with each other.

CONCLUSIONS

EEG E/I estimates offer an intriguing opportunity to map cortical excitability non-invasively, with high spatio-temporal resolution and with a stimulus independent approach. While different EEG E/I estimates may reflect the activity of diverse excitatory-inhibitory circuits, spatial phase synchrony in the gamma band is the measure that best captures excitability changes in the primary motor cortex.

Deep brain stimulation of the central thalamus restores arousal and motivation in a zolpidem-responsive patient with akinetic mutism after severe brain injury

After severe brain injury, zolpidem is known to cause spectacular, often short-lived, restorations of brain functions in a small subgroup of patients. Previously, we showed that these zolpidem-induced neurological recoveries can be paralleled by significant changes in functional connectivity throughout the brain. Deep brain stimulation (DBS) is a neurosurgical intervention known to modulate functional connectivity in a wide variety of neurological disorders. In this study, we used DBS to restore arousal and motivation in a zolpidem-responsive patient with severe brain injury and a concomitant disorder of diminished motivation, more than 10 years after surviving hypoxic ischemia. We found that DBS of the central thalamus, targeted at the centromedian-parafascicular complex, immediately restored arousal and was able to transition the patient from a state of deep sleep to full wakefulness. Moreover, DBS was associated with temporary restoration of communication and ability to walk and eat in an otherwise wheelchair-bound and mute patient. With the use of magnetoencephalography (MEG), we revealed that DBS was generally associated with a marked decrease in aberrantly high levels of functional connectivity throughout the brain, mimicking the effects of zolpidem. These results imply that 'pathological hyperconnectivity' after severe brain injury can be associated with reduced arousal and behavioral performance and that DBS is able to modulate connectivity towards a 'healthier baseline' with lower synchronization, and, can restore functional brain networks long after severe brain injury. The presence of hyperconnectivity after brain injury may be a possible future marker for a patient's responsiveness for restorative interventions, such as DBS, and suggests that lower degrees of overall brain synchronization may be conducive to cognition and behavioral responsiveness.

A disinhibitory circuit mechanism explains a general principle of peak performance during mid-level arousal

Perceptual decision-making is highly dependent on the momentary arousal state of the brain, which fluctuates over time on a scale of hours, minutes, and even seconds. The textbook relationship between momentary arousal and task performance is captured by an inverted U-shape, as put forward in the Yerkes-Dodson law. This law suggests optimal performance at moderate levels of arousal and impaired performance at low or high arousal levels. However, despite its popularity, the evidence for this relationship in humans is mixed at best. Here, we use pupil-indexed arousal and performance data from various perceptual decision-making tasks to provide converging evidence for the inverted U-shaped relationship between spontaneous arousal fluctuations and performance across different decision types (discrimination, detection) and sensory modalities (visual, auditory). To further understand this relationship, we built a neurobiologically plausible mechanistic model and show that it is possible to reproduce our findings by incorporating two types of interneurons that are both modulated by an arousal signal. The model architecture produces two dynamical regimes under the influence of arousal: one regime in which performance increases with arousal and another regime in which performance decreases with arousal, together forming an inverted U-shaped arousal-performance relationship. We conclude that the inverted U-shaped arousal-performance relationship is a general and robust property of sensory processing. It might be brought about by the influence of arousal on two types of interneurons that together act as a disinhibitory pathway for the neural populations that encode the available sensory evidence used for the decision.

Ramping dynamics and theta oscillations reflect dissociable signatures during rule-guided human behavior

Contextual cues and prior evidence guide human goal-directed behavior. The neurophysiological mechanisms that implement contextual priors to guide subsequent actions in the human brain remain unclear. Using intracranial electroencephalography (iEEG), we demonstrate that increasing uncertainty introduces a shift from a purely oscillatory to a mixed processing regime with an additional ramping component. Oscillatory and ramping dynamics reflect dissociable signatures, which likely differentially contribute to the encoding and transfer of different cognitive variables in a cue-guided motor task. The results support the idea that prefrontal activity encodes rules and ensuing actions in distinct coding subspaces, while theta oscillations synchronize the prefrontal-motor network, possibly to guide action execution. Collectively, our results reveal how two key features of large-scale neural population activity, namely continuous ramping dynamics and oscillatory synchrony, jointly support rule-guided human behavior.

Neurobiological Underpinnings of Hyperarousal in Depression: A Comprehensive Review

Patients with major depressive disorder (MDD) exhibit an abnormal physiological arousal pattern known as hyperarousal, which may contribute to their depressive symptoms. However, the neurobiological mechanisms linking this abnormal arousal to depressive symptoms are not yet fully understood. In this review, we summarize the physiological and neural features of arousal, and review the literature indicating abnormal arousal in depressed patients. Evidence suggests that a hyperarousal state in depression is characterized by abnormalities in sleep behavior, physiological (e.g., heart rate, skin conductance, pupil diameter) and electroencephalography (EEG) features, and altered activity in subcortical (e.g., hypothalamus and locus coeruleus) and cortical regions. While recent studies highlight the importance of subcortical-cortical interactions in arousal, few have explored the relationship between subcortical-cortical interactions and hyperarousal in depressed patients. This gap limits our understanding of the neural mechanism through which hyperarousal affects depressive symptoms, which involves various cognitive processes and the cerebral cortex. Based on the current literature, we propose that the hyperconnectivity in the thalamocortical circuit may contribute to both the hyperarousal pattern and depressive symptoms. Future research should investigate the relationship between thalamocortical connections and abnormal arousal in depression, and explore its implications for non-invasive treatments for depression.

Complexity and 1/f slope jointly reflect brain states

Characterization of brain states is essential for understanding its functioning in the absence of external stimuli. Brain states differ on their balance between excitation and inhibition, and on the diversity of their activity patterns. These can be respectively indexed by 1/f slope and Lempel-Ziv complexity (LZc). However, whether and how these two brain state properties relate remain elusive. Here we analyzed the relation between 1/f slope and LZc with two in-silico approaches and in both rat EEG and monkey ECoG data. We contrasted resting state with propofol anesthesia, which directly modulates the excitation-inhibition balance. We found convergent results among simulated and empirical data, showing a strong, inverse and non trivial monotonic relation between 1/f slope and complexity, consistent at both ECoG and EEG scales. We hypothesize that differentially entropic regimes could underlie the link between the excitation-inhibition balance and the vastness of the repertoire of brain systems.

Effects of pupil size as manipulated through ipRGC activation on visual processing

The size of the eyes' pupils determines how much light enters the eye and also how well this light is focused. Through this route, pupil size shapes the earliest stages of visual processing. Yet causal effects of pupil size on vision are poorly understood and rarely studied. Here we introduce a new way to manipulate pupil size, which relies on activation of intrinsically photosensitive retinal ganglion cells (ipRGCs) to induce sustained pupil constriction. We report the effects of both experimentally induced and spontaneous changes in pupil size on visual processing as measured through EEG. We compare these to the effects of stimulus intensity and covert visual attention, because previous studies have shown that these factors all have comparable effects on some common measures of early visual processing, such as detection performance and steady-state visual evoked potentials; yet it is still unclear whether these are superficial similarities, or rather whether they reflect similar underlying processes. Using a mix of neural-network decoding, ERP analyses, and time-frequency analyses, we find that induced pupil size, spontaneous pupil size, stimulus intensity, and covert visual attention all affect EEG responses, mainly over occipital and parietal electrodes, but-crucially-that they do so in qualitatively different ways. Induced and spontaneous pupil-size changes mainly modulate activity patterns (but not overall power or intertrial coherence) in the high-frequency beta range; this may reflect an effect of pupil size on oculomotor activity and/ or visual processing. In addition, spontaneous (but not induced) pupil size tends to correlate positively with intertrial coherence in the alpha band; this may reflect a non-causal relationship, mediated by arousal. Taken together, our findings suggest that pupil size has qualitatively different effects on visual processing from stimulus intensity and covert visual attention. This shows that pupil size as manipulated through ipRGC activation strongly affects visual processing, and provides concrete starting points for further study of this important yet understudied earliest stage of visual processing.

Catecholaminergic neuromodulation and selective attention jointly shape perceptual decision-making

Perceptual decisions about sensory input are influenced by fluctuations in ongoing neural activity, most prominently driven by attention and neuromodulator systems. It is currently unknown if neuromodulator activity and attention differentially modulate perceptual decision-making and/or whether neuromodulatory systems in fact control attentional processes. To investigate the effects of two distinct neuromodulatory systems and spatial attention on perceptual decisions, we pharmacologically elevated cholinergic (through donepezil) and catecholaminergic (through atomoxetine) levels in humans performing a visuo-spatial attention task, while we measured electroencephalography (EEG). Both attention and catecholaminergic enhancement improved decision-making at the behavioral and algorithmic level, as reflected in increased perceptual sensitivity and the modulation of the drift rate parameter derived from drift diffusion modeling. Univariate analyses of EEG data time-locked to the attentional cue, the target stimulus, and the motor response further revealed that attention and catecholaminergic enhancement both modulated pre-stimulus cortical excitability, cue- and stimulus-evoked sensory activity, as well as parietal evidence accumulation signals. Interestingly, we observed both similar, unique, and interactive effects of attention and catecholaminergic neuromodulation on these behavioral, algorithmic, and neural markers of the decision-making process. Thereby, this study reveals an intricate relationship between attentional and catecholaminergic systems and advances our understanding about how these systems jointly shape various stages of perceptual decision-making.

Pupil-linked arousal modulates network-level EEG signatures of attention reorienting during immersive multitasking

Objective. When multitasking, we must dynamically reorient our attention between different tasks. Attention reorienting is thought to arise through interactions of physiological arousal and brain-wide network dynamics. In this study, we investigated the relationship between pupil-linked arousal and electroencephalography (EEG) brain dynamics in a multitask driving paradigm conducted in virtual reality. We hypothesized that there would be an interaction between arousal and EEG dynamics and that this interaction would correlate with multitasking performance.Approach. We collected EEG and eye tracking data while subjects drove a motorcycle through a simulated city environment, with the instructions to count the number of target images they observed while avoiding crashing into a lead vehicle. The paradigm required the subjects to continuously reorient their attention between the two tasks. Subjects performed the paradigm under two conditions, one more difficult than the other.Main results. We found that task difficulty did not strongly correlate with pupil-linked arousal, and overall task performance increased as arousal level increased. A single-trial analysis revealed several interesting relationships between pupil-linked arousal and task-relevant EEG dynamics. Employing exact low-resolution electromagnetic tomography, we found that higher pupil-linked arousal led to greater EEG oscillatory activity, especially in regions associated with the dorsal attention network and ventral attention network (VAN). Consistent with our hypothesis, we found a relationship between EEG functional connectivity and pupil-linked arousal as a function of multitasking performance. Specifically, we found decreased functional connectivity between regions in the salience network (SN) and the VAN as pupil-linked arousal increased, suggesting that improved multitasking performance at high arousal levels may be due to a down-regulation in coupling between the VAN and the SN. Our results suggest that when multitasking, our brain rebalances arousal-based reorienting so that individual task demands can be met without prematurely reorienting to competing tasks.

Pupil size predicts the onset of exploration in brain and behavior

In uncertain environments, intelligent decision-makers exploit actions that have been rewarding in the past, but also explore actions that could be even better. Several neuromodulatory systems are implicated in exploration, based, in part, on work linking exploration to pupil size-a peripheral correlate of neuromodulatory tone and index of arousal. However, pupil size could instead track variables that make exploration more likely, like volatility or reward, without directly predicting either exploration or its neural bases. Here, we simultaneously measured pupil size, exploration, and neural population activity in the prefrontal cortex while two rhesus macaques explored and exploited in a dynamic environment. We found that pupil size under constant luminance specifically predicted the onset of exploration, beyond what could be explained by reward history. Pupil size also predicted disorganized patterns of prefrontal neural activity at both the single neuron and population levels, even within periods of exploitation. Ultimately, our results support a model in which pupil-linked mechanisms promote the onset of exploration via driving the prefrontal cortex through a critical tipping point where prefrontal control dynamics become disorganized and exploratory decisions are possible.

Reward responsiveness in autism and autistic traits - Evidence from neuronal, autonomic, and behavioural levels

Autism has been linked to atypicalities in reward processing, especially in the social domain. However, results are heterogeneous, and their interpretation is hindered by the use of personally non-relevant social rewards. In this study we investigated behavioural (reaction times), neuronal (event-related potentials), and autonomic (pupil sizes) responses to personally relevant social rewards, money, and neutral outcomes in 26 autistic and 53 non-autistic subjects varying in levels of autistic traits. As hypothesised and preregistered, autism and autistic traits did not differently influence responses to social, monetary, or neutral outcomes on either response level. While groups did not differ in behaviour (reaction times), autism was linked to generally enhanced brain responses in early anticipation and larger pupil constrictions in reward reception. Together, these results suggest that when using personally relevant stimuli, autism is linked to generally preserved, although less neuronally efficient processing of rewards. Considering the role of social relevance in reward processing, we propose an interpretation of contradictory evidence from clinical practice and empirical research.

Arousal and Locomotion Differently Modulate Activity of Somatostatin Neurons across Cortex

Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin (SOM)-expressing inhibitory interneurons are particularly well situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM-expressing neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex, in mice. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not in PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior.

Circadian rhythms in auditory hallucinations and psychosis

Circadian rhythms are imprinted in all organisms and influence virtually all aspects of physiology and behavior in adaptation to the 24-h day-night cycle. This recognition of a circadian timekeeping system permeating essentially all healthy functioning of body and mind quickly leads to the realization that, in turn, human ailments should be probed for the degree to which they are rooted in or marked by disruptions and dysregulations of circadian clock functions in the human body. In this review, we will focus on psychosis as a key mental illness and foremost one of its cardinal symptoms: auditory hallucinations. We will discuss recent empirical evidence and conceptual advances probing the potential role of circadian disruption in auditory hallucinations. Moreover, a dysbalance in excitation and inhibition within cortical networks, which in turn drive a disinhibition of dopaminergic signaling, will be highlighted as central physiological mechanism. Finally, we will propose two avenues for experimentally intervening on the circadian influences to potentially alleviate hallucinations in psychotic disorders.

A spatiotemporal complexity architecture of human brain activity

The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.

Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability

Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.

Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks

Neurons in vitro connect to each other and form neural networks that display emergent electrophysiological activity. This activity begins as spontaneous uncorrelated firing in the early phase of development, and as functional excitatory and inhibitory synapses mature, the activity typically emerges as spontaneous network bursts. Network bursts are events of coordinated global activation among many neurons interspersed with periods of silencing and are important for synaptic plasticity, neural information processing, and network computation. While bursting is the consequence of balanced excitatory-inhibitory (E/I) interactions, the functional mechanisms underlying their evolution from physiological to potentially pathophysiological states, such as decreasing or increasing in synchrony, are still poorly understood. Synaptic activity, especially that related to maturity of E/I synaptic transmission, is known to strongly influence these processes. In this study, we used selective chemogenetic inhibition to target and disrupt excitatory synaptic transmission in in vitro neural networks to study functional response and recovery of spontaneous network bursts over time. We found that over time, inhibition resulted in increases in both network burstiness and synchrony. Our results indicate that the disruption in excitatory synaptic transmission during early network development likely affected inhibitory synaptic maturity which resulted in an overall decrease in network inhibition at later stages. These findings lend support to the importance of E/I balance in maintaining physiological bursting dynamics and, conceivably, information processing capacity in neural networks.

Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms

Expectations, such as those arising from either learned rules or recent stimulus regularities, can bias subsequent auditory perception in diverse ways. However, it is not well understood if and how these diverse effects depend on the source of the expectations. Further, it is unknown whether different sources of bias use the same or different computational and physiological mechanisms. We examined how rule-based and stimulus-based expectations influenced behavior and pupil-linked arousal, a marker of certain forms of expectation-based processing, of human subjects performing an auditory frequency-discrimination task. Rule-based cues consistently biased choices and response times (RTs) toward the more-probable stimulus. In contrast, stimulus-based cues had a complex combination of effects, including choice and RT biases toward and away from the frequency of recently presented stimuli. These different behavioral patterns also had: 1) distinct computational signatures, including different modulations of key components of a novel form of a drift-diffusion decision model and 2) distinct physiological signatures, including substantial bias-dependent modulations of pupil size in response to rule-based but not stimulus-based cues. These results imply that different sources of expectations can modulate auditory processing via distinct mechanisms: one that uses arousal-linked, rule-based information and another that uses arousal-independent, stimulus-based information to bias the speed and accuracy of auditory perceptual decisions.

Prior information about upcoming stimuli can bias our perception of those stimuli. Whether different sources of prior information bias perception in similar or distinct ways is not well understood. We compared the influence of two kinds of prior information on tone-frequency discrimination: rule-based cues, in the form of explicit information about the most-likely identity of the upcoming tone; and stimulus-based cues, in the form of sequences of tones presented before the to-be-discriminated tone. Although both types of prior information biased auditory decision-making, they demonstrated distinct behavioral, computational, and physiological signatures. Our results suggest that the brain processes prior information in a form-specific manner rather than utilizing a general-purpose prior. Such form-specific processing has implications for understanding decision biases real-world contexts, in which prior information comes from many different sources and modalities.

Clinical and neurophysiological effects of central thalamic deep brain stimulation in the minimally conscious state after severe brain injury

Deep brain stimulation (DBS) of the central thalamus is an experimental treatment for restoration of impaired consciousness in patients with severe acquired brain injury. Previous results of experimental DBS are heterogeneous, but significant improvements in consciousness have been reported. However, the mechanism of action of DBS remains unknown. We used magnetoencephalography to study the direct effects of DBS of the central thalamus on oscillatory activity and functional connectivity throughout the brain in a patient with a prolonged minimally conscious state. Different DBS settings were used to improve consciousness, including two different stimulation frequencies (50 Hz and 130 Hz) with different effective volumes of tissue activation within the central thalamus. While both types of DBS resulted in a direct increase in arousal, we found that DBS with a lower frequency (50 Hz) and larger volume of tissue activation was associated with a stronger increase in functional connectivity and neural variability throughout the brain. Moreover, this form of DBS was associated with improvements in visual pursuit, a reduction in spasticity, and improvement of swallowing, eight years after loss of consciousness. However, after DBS, all neurophysiological markers remained significantly lower than in healthy controls and objective increases in consciousness remained limited. Our findings provide new insights on the mechanistic understanding of neuromodulatory effects of DBS of the central thalamus in humans and suggest that DBS can re-activate dormant functional brain networks, but that the severely injured stimulated brain still lacks the ability to serve cognitive demands.

Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease?

Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power-based models (76.9% [60.4-93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.

Spectral Fingerprints of Cortical Neuromodulation

Pupil size has been established as a versatile marker of noradrenergic and cholinergic neuromodulation, which has profound effects on neuronal processing, cognition, and behavior. However, little is known about the cortical control and effects of pupil-linked neuromodulation. Here, we show that pupil dynamics are tightly coupled to temporally, spectrally, and spatially specific modulations of local and large-scale cortical population activity in the human brain. We quantified the dynamics of band-limited cortical population activity in resting human subjects using magnetoencephalography and investigated how neural dynamics were linked to simultaneously recorded pupil dynamics. Our results show that pupil-linked neuromodulation does not merely affect cortical population activity in a stereotypical fashion. Instead, we identified three frontal, precentral, and occipitoparietal networks, in which local population activity with distinct spectral profiles in the theta, beta, and alpha bands temporally preceded and followed changes in pupil size. Furthermore, we found that amplitude coupling at ∼16 Hz in a large-scale frontoparietal network predicted pupil dynamics. Our results unravel network-specific spectral fingerprints of cortical neuromodulation in the human brain that likely reflect both the causes and effects of neuromodulation.SIGNIFICANCE STATEMENT Brain function is constantly affected by modulatory neurotransmitters. Pupil size has been established as a versatile marker of noradrenergic and cholinergic neuromodulation. However, because the cortical correlates of pupil dynamics are largely unknown, fundamental questions remain unresolved. Which cortical networks control pupil-linked neuromodulation? Does neuromodulation affect cortical activity in a stereotypical or region-specific fashion? To address this, we quantified the dynamics of cortical population activity in human subjects using magnetoencephalography. We found that pupil dynamics are coupled to highly specific modulations of local and large-scale cortical activity in the human brain. We identified four cortical networks with distinct spectral profiles that temporally predicted and followed pupil size dynamics. These effects likely reflect both the cortical control and effect of neuromodulation.

Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model

Arousal levels strongly affect task performance. Yet, what arousal level is optimal for a task depends on its difficulty. Easy task performance peaks at higher arousal levels, whereas performance on difficult tasks displays an inverted U-shape relationship with arousal, peaking at medium arousal levels, an observation first made by Yerkes and Dodson in 1908. It is commonly proposed that the noradrenergic locus coeruleus system regulates these effects on performance through a widespread release of noradrenaline resulting in changes of cortical gain. This account, however, does not explain why performance decays with high arousal levels only in difficult, but not in simple tasks. Here, we present a mechanistic model that revisits the Yerkes-Dodson effect from a sensory perspective: a deep convolutional neural network augmented with a global gain mechanism reproduced the same interaction between arousal state and task difficulty in its performance. Investigating this model revealed that global gain states differentially modulated sensory information encoding across the processing hierarchy, which explained their differential effects on performance on simple versus difficult tasks. These findings offer a novel hierarchical sensory processing account of how, and why, arousal state affects task performance.

Functional Coupling of the Locus Coeruleus Is Linked to Successful Cognitive Control

The locus coeruleus (LC) is a brainstem structure that sends widespread efferent projections throughout the mammalian brain. The LC constitutes the major source of noradrenaline (NE), a modulatory neurotransmitter that is crucial for fundamental brain functions such as arousal, attention, and cognitive control. This role of the LC-NE is traditionally not believed to reflect functional influences on the frontoparietal network or the striatum, but recent advances in chemogenetic manipulations of the rodent brain have challenged this notion. However, demonstrations of LC-NE functional connectivity with these areas in the human brain are surprisingly sparse. Here, we close this gap. Using an established emotional stroop task, we directly compared trials requiring response conflict control with trials that did not require this, but were matched for visual stimulus properties, response modality, and controlled for pupil dilation differences across both trial types. We found that LC-NE functional coupling with the parietal cortex and regions of the striatum is substantially enhanced during trials requiring response conflict control. Crucially, the strength of this functional coupling was directly related to individual reaction time differences incurred by conflict resolution. Our data concur with recent rodent findings and highlight the importance of converging evidence between human and nonhuman neurophysiology to further understand the neural systems supporting adaptive and maladaptive behavior in health and disease.

Power signatures of habenular neuronal signals in patients with bipolar or unipolar depressive disorders correlate with their disease severity

The habenula is an epithalamic structure implicated in negative reward mechanisms and plays a downstream modulatory role in regulation of dopaminergic and serotonergic functions. Human and animal studies show its hyperactivity in depression which is curtailed by the antidepressant response of ketamine. Deep brain stimulation of habenula (DBS) for major depression have also shown promising results. However, direct neuronal activity of habenula in human studies have rarely been reported. Here, in a cross-sectional design, we acquired both spontaneous resting state and emotional task-induced neuronal recordings from habenula from treatment resistant depressed patients undergoing DBS surgery. We first characterise the aperiodic component (1/f slope) of the power spectrum, interpreted to signify excitation-inhibition balance, in resting and task state. This aperiodicity for left habenula correlated between rest and task and which was significantly positively correlated with depression severity. Time-frequency responses to the emotional picture viewing task show condition differences in beta and gamma frequencies for left habenula and alpha for right habenula. Notably, alpha activity for right habenula was negatively correlated with depression severity. Overall, from direct habenular recordings, we thus show findings convergent with depression models of aberrant excitatory glutamatergic output of the habenula driving inhibition of monoaminergic systems.

Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has so far only been characterized for a few selected brain regions. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by monotonic and inverted U relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

Arousal States as a Key Source of Variability in Speech Perception and Learning

The human brain exhibits the remarkable ability to categorize speech sounds into distinct, meaningful percepts, even in challenging tasks like learning non-native speech categories in adulthood and hearing speech in noisy listening conditions. In these scenarios, there is substantial variability in perception and behavior, both across individual listeners and individual trials. While there has been extensive work characterizing stimulus-related and contextual factors that contribute to variability, recent advances in neuroscience are beginning to shed light on another potential source of variability that has not been explored in speech processing. Specifically, there are task-independent, moment-to-moment variations in neural activity in broadly-distributed cortical and subcortical networks that affect how a stimulus is perceived on a trial-by-trial basis. In this review, we discuss factors that affect speech sound learning and moment-to-moment variability in perception, particularly arousal states—neurotransmitter-dependent modulations of cortical activity. We propose that a more complete model of speech perception and learning should incorporate subcortically-mediated arousal states that alter behavior in ways that are distinct from, yet complementary to, top-down cognitive modulations. Finally, we discuss a novel neuromodulation technique, transcutaneous auricular vagus nerve stimulation (taVNS), which is particularly well-suited to investigating causal relationships between arousal mechanisms and performance in a variety of perceptual tasks. Together, these approaches provide novel testable hypotheses for explaining variability in classically challenging tasks, including non-native speech sound learning.

Noradrenergic modulation of rhythmic neural activity shapes selective attention

During moments involving selective attention, the thalamus orchestrates the preferential processing of prioritized information by coordinating rhythmic neural activity within a distributed frontoparietal network. The timed release of neuromodulators from subcortical structures dynamically sculpts neural synchronization in thalamocortical networks to meet current attentional demands. In particular, noradrenaline modulates the balance of cortical excitation and inhibition, as reflected by thalamocortical alpha synchronization (~8-12 Hz). These neuromodulatory adjustments facilitate the selective processing of prioritized information. Thus, by disrupting effective rhythmic coordination in attention networks, age-related locus coeruleus (LC) degeneration can impair higher levels of neural processing. In sum, findings across different levels of analysis and modalities shed light on how the noradrenergic modulation of neural synchronization helps to shape selective attention.

EEG Neurofeedback for Anxiety Disorders and Post-Traumatic Stress Disorders: A Blueprint for a Promising Brain-Based Therapy

PURPOSE OF REVIEW

This review provides an overview of current knowledge and understanding of EEG neurofeedback for anxiety disorders and post-traumatic stress disorders.

RECENT FINDINGS

The manifestations of anxiety disorders and post-traumatic stress disorders (PTSD) are associated with dysfunctions of neurophysiological stress axes and brain arousal circuits, which are important dimensions of the research domain criteria (RDoC). Even if the pathophysiology of these disorders is complex, one of its defining signatures is behavioral and physiological over-arousal. Interestingly, arousal-related brain activity can be modulated by electroencephalogram-based neurofeedback (EEG NF), a non-pharmacological and non-invasive method that involves neurocognitive training through a brain-computer interface (BCI). EEG NF is characterized by a simultaneous learning process where both patient and computer are involved in modifying neuronal activity or connectivity, thereby improving associated symptoms of anxiety and/or over-arousal. Positive effects of EEG NF have been described for both anxiety disorders and PTSD, yet due to a number of methodological issues, it remains unclear whether symptom improvement is the direct result of neurophysiological changes targeted by EEG NF. Thus, in this work we sought to bridge current knowledge on brain mechanisms of arousal with past and present EEG NF therapies for anxiety and PTSD. In a nutshell, we discuss the neurophysiological mechanisms underlying the effects of EEG NF in anxiety disorder and PTSD, the methodological strengths/weaknesses of existing EEG NF randomized controlled trials for these disorders, and the neuropsychological factors that may impact NF training success.

Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

A hallmark of electrophysiological brain activity is its 1/f-like spectrum - power decreases with increasing frequency. The steepness of this 'roll-off' is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first establish that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general (i.e., anaesthesia-driven) changes in E:I balance. Building on the EEG spectral exponent as a viable marker of E:I, we then demonstrate its sensitivity to the focus of selective attention in an EEG experiment during which participants detected targets in simultaneous audio-visual noise. In addition to these endogenous changes in E:I balance, EEG spectral exponents over auditory and visual sensory cortices also tracked auditory and visual stimulus spectral exponents, respectively. Individuals' degree of this selective stimulus-brain coupling in spectral exponents predicted behavioural performance. Our results highlight the rich information contained in 1/f-like neural activity, providing a window into diverse neural processes previously thought to be inaccessible in non-invasive human recordings.

Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

A hallmark of electrophysiological brain activity is its 1/f-like spectrum - power decreases with increasing frequency. The steepness of this 'roll-off' is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first establish that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general (i.e., anaesthesia-driven) changes in E:I balance. Building on the EEG spectral exponent as a viable marker of E:I, we then demonstrate its sensitivity to the focus of selective attention in an EEG experiment during which participants detected targets in simultaneous audio-visual noise. In addition to these endogenous changes in E:I balance, EEG spectral exponents over auditory and visual sensory cortices also tracked auditory and visual stimulus spectral exponents, respectively. Individuals' degree of this selective stimulus-brain coupling in spectral exponents predicted behavioural performance. Our results highlight the rich information contained in 1/f-like neural activity, providing a window into diverse neural processes previously thought to be inaccessible in non-invasive human recordings.

Spectral signature and behavioral consequence of spontaneous shifts of pupil-linked arousal in human

Arousal levels perpetually rise and fall spontaneously. How markers of arousal—pupil size and frequency content of brain activity—relate to each other and influence behavior in humans is poorly understood. We simultaneously monitored magnetoencephalography and pupil in healthy volunteers at rest and during a visual perceptual decision-making task. Spontaneously varying pupil size correlates with power of brain activity in most frequency bands across large-scale resting state cortical networks. Pupil size recorded at prestimulus baseline correlates with subsequent shifts in detection bias (c) and sensitivity (d’). When dissociated from pupil-linked state, prestimulus spectral power of resting state networks still predicts perceptual behavior. Fast spontaneous pupil constriction and dilation correlate with large-scale brain activity as well but not perceptual behavior. Our results illuminate the relation between central and peripheral arousal markers and their respective roles in human perceptual decision-making.

Neural attentional-filter mechanisms of listening success in middle-aged and older individuals

Successful listening crucially depends on intact attentional filters that separate relevant from irrelevant information. Research into their neurobiological implementation has focused on two potential auditory filter strategies: the lateralization of alpha power and selective neural speech tracking. However, the functional interplay of the two neural filter strategies and their potency to index listening success in an ageing population remains unclear. Using electroencephalography and a dual-talker task in a representative sample of listeners (N = 155; age=39-80 years), we here demonstrate an often-missed link from single-trial behavioural outcomes back to trial-by-trial changes in neural attentional filtering. First, we observe preserved attentional-cue-driven modulation of both neural filters across chronological age and hearing levels. Second, neural filter states vary independently of one another, demonstrating complementary neurobiological solutions of spatial selective attention. Stronger neural speech tracking but not alpha lateralization boosts trial-to-trial behavioural performance. Our results highlight the translational potential of neural speech tracking as an individualized neural marker of adaptive listening behaviour.

Neuronal correlates of the subjective experience of attention

The effect of top–down attention on stimulus‐evoked responses and alpha oscillations and the association between arousal and pupil diameter are well established. However, the relationship between these indices, and their contribution to the subjective experience of attention, remains largely unknown. Participants performed a sustained (10–30 s) attention task in which rare (10%) targets were detected within continuous tactile stimulation (16 Hz). Trials were followed by attention ratings on an 8‐point visual scale. Attention ratings correlated negatively with contralateral somatosensory alpha power and positively with pupil diameter. The effect of pupil diameter on attention ratings extended into the following trial, reflecting a sustained aspect of attention related to vigilance. The effect of alpha power did not carry over to the next trial and furthermore mediated the association between pupil diameter and attention ratings. Variations in steady‐state amplitude reflected stimulus processing under the influence of alpha oscillations but were only weakly related to subjective ratings of attention. Together, our results show that both alpha power and pupil diameter are reflected in the subjective experience of attention, albeit on different time spans, while continuous stimulus processing might not contribute to the experience of attention.

Dynamic relationships between spontaneous and evoked electrophysiological activity

Spontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

Circadian fluctuations in glucocorticoid level predict perceptual discrimination sensitivity

Slow neurobiological rhythms, such as the circadian secretion of glucocorticoid (GC) hormones, modulate a variety of body functions. Whether and how endocrine fluctuations also exert an influence on perceptual abilities is largely uncharted. Here, we show that phasic increases in GC availability prove beneficial to auditory discrimination. In an age-varying sample of N = 68 healthy human participants, we characterize the covariation of saliva cortisol with perceptual sensitivity in an auditory pitch discrimination task at five time points across the sleep-wake cycle. First, momentary saliva cortisol levels were captured well by the time relative to wake-up and overall sleep duration. Second, within individuals, higher cortisol levels just prior to behavioral testing predicted better pitch discrimination ability, expressed as a steepened psychometric curve. This effect of GCs held under a set of statistical controls. Our results pave the way for more in-depth studies on neuroendocrinological determinants of sensory encoding and perception.

Thalamocortical excitability modulation guides human perception under uncertainty

Knowledge about the relevance of environmental features can guide stimulus processing. However, it remains unclear how processing is adjusted when feature relevance is uncertain. We hypothesized that (a) heightened uncertainty would shift cortical networks from a rhythmic, selective processing-oriented state toward an asynchronous ("excited") state that boosts sensitivity to all stimulus features, and that (b) the thalamus provides a subcortical nexus for such uncertainty-related shifts. Here, we had young adults attend to varying numbers of task-relevant features during EEG and fMRI acquisition to test these hypotheses. Behavioral modeling and electrophysiological signatures revealed that greater uncertainty lowered the rate of evidence accumulation for individual stimulus features, shifted the cortex from a rhythmic to an asynchronous/excited regime, and heightened neuromodulatory arousal. Crucially, this unified constellation of within-person effects was dominantly reflected in the uncertainty-driven upregulation of thalamic activity. We argue that neuromodulatory processes involving the thalamus play a central role in how the brain modulates neural excitability in the face of momentary uncertainty.

Graded recruitment of pupil-linked neuromodulation by parametric stimulation of the vagus nerve

Vagus nerve stimulation (VNS) is thought to affect neural activity by recruiting brain-wide release of neuromodulators. VNS is used in treatment-resistant epilepsy, and is increasingly being explored for other disorders, such as depression, and as a cognitive enhancer. However, the promise of VNS is only partially fulfilled due to a lack of mechanistic understanding of the transfer function between stimulation parameters and neuromodulatory response, together with a lack of biosensors for assaying stimulation efficacy in real time. We here develop an approach to VNS in head-fixed mice on a treadmill and show that pupil dilation is a reliable and convenient biosensor for VNS-evoked cortical neuromodulation. In an 'optimal' zone of stimulation parameters, current leakage and off-target effects are minimized and the extent of pupil dilation tracks VNS-evoked basal-forebrain cholinergic axon activity in neocortex. Thus, pupil dilation is a sensitive readout of the moment-by-moment, titratable effects of VNS on brain state.

Behavior needs neural variability

Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.

The Locus Coeruleus- Norepinephrine System in Stress and Arousal: Unraveling Historical, Current, and Future Perspectives

Arousal may be understood on a spectrum, with excessive sleepiness, cognitive dysfunction, and inattention on one side, a wakeful state in the middle, and hypervigilance, panic, and psychosis on the other side. However, historically, the concepts of arousal and stress have been challenging to define as measurable experimental variables. Divergent efforts to study these subjects have given rise to several disciplines, including neurobiology, neuroendocrinology, and cognitive neuroscience. We discuss technological advancements that chronologically led to our current understanding of the arousal system, focusing on the multifaceted nucleus locus coeruleus. We share our contemporary perspective and the hypotheses of others in the context of our current technological capabilities and future developments that will be required to move forward in this area of research.

Transcutaneous Vagus Nerve Stimulation in Humans Induces Pupil Dilation and Attenuates Alpha Oscillations

Vagus nerve stimulation (VNS) is widely used to treat drug-resistant epilepsy and depression. While the precise mechanisms mediating its long-term therapeutic effects are not fully resolved, they likely involve locus coeruleus (LC) stimulation via the nucleus of the solitary tract, which receives afferent vagal inputs. In rats, VNS elevates LC firing and forebrain noradrenaline levels, whereas LC lesions suppress VNS therapeutic efficacy. Noninvasive transcutaneous VNS (tVNS) uses electrical stimulation that targets the auricular branch of the vagus nerve at the cymba conchae of the ear. However, the extent to which tVNS mimics VNS remains unclear. Here, we investigated the short-term effects of tVNS in healthy human male volunteers (n = 24), using high-density EEG and pupillometry during visual fixation at rest. We compared short (3.4 s) trials of tVNS to sham electrical stimulation at the earlobe (far from the vagus nerve branch) to control for somatosensory stimulation. Although tVNS and sham stimulation did not differ in subjective intensity ratings, tVNS led to robust pupil dilation (peaking 4-5 s after trial onset) that was significantly higher than following sham stimulation. We further quantified, using parallel factor analysis, how tVNS modulates idle occipital alpha (8-13Hz) activity identified in each participant. We found greater attenuation of alpha oscillations by tVNS than by sham stimulation. This demonstrates that tVNS reliably induces pupillary and EEG markers of arousal beyond the effects of somatosensory stimulation, thus supporting the hypothesis that tVNS elevates noradrenaline and other arousal-promoting neuromodulatory signaling, and mimics invasive VNS.SIGNIFICANCE STATEMENT Current noninvasive brain stimulation techniques are mostly confined to modulating cortical activity, as is typical with transcranial magnetic or transcranial direct/alternating current electrical stimulation. Transcutaneous vagus nerve stimulation (tVNS) has been proposed to stimulate subcortical arousal-promoting nuclei, though previous studies yielded inconsistent results. Here we show that short (3.4 s) tVNS pulses in naive healthy male volunteers induced transient pupil dilation and attenuation of occipital alpha oscillations. These markers of brain arousal are in line with the established effects of invasive VNS on locus coeruleus-noradrenaline signaling, and support that tVNS mimics VNS. Therefore, tVNS can be used as a tool for studying how endogenous subcortical neuromodulatory signaling affects human cognition, including perception, attention, memory, and decision-making; and also for developing novel clinical applications.

Boosts in brain signal variability track liberal shifts in decision bias

Adopting particular decision biases allows organisms to tailor their choices to environmental demands. For example, a liberal response strategy pays off when target detection is crucial, whereas a conservative strategy is optimal for avoiding false alarms. Using conventional time-frequency analysis of human electroencephalographic (EEG) activity, we previously showed that bias setting entails adjustment of evidence accumulation in sensory regions (Kloosterman et al., 2019), but the presumed prefrontal signature of a conservative-to-liberal bias shift has remained elusive. Here, we show that a liberal bias shift is reflected in a more unconstrained neural regime (boosted entropy) in frontal regions that is suited to the detection of unpredictable events. Overall EEG variation, spectral power and event-related potentials could not explain this relationship, highlighting that moment-to-moment neural variability uniquely tracks bias shifts. Neural variability modulation through prefrontal cortex appears instrumental for permitting an organism to adapt its biases to environmental demands.