On the interrelation of 1/f neural noise and norepinephrine system activity during motor response inhibition

Tracing conflict-induced cognitive-control adjustments over time using aperiodic EEG activity

Cognitive-control theories assume that the experience of response conflict can trigger control adjustments. However, while some approaches focus on adjustments that impact the selection of the present response (in trial N), other approaches focus on adjustments in the next upcoming trial  (N + 1). We aimed to trace control adjustments over time by quantifying cortical noise by means of the fitting oscillations and one over f algorithm, a measure of aperiodic activity. As predicted, conflict trials increased the aperiodic exponent in a large sample of 171 healthy adults, thus indicating noise reduction. While this adjustment was visible in trial N already, it did not affect response selection before the next trial. This suggests that control adjustments do not affect ongoing response-selection processes but prepare the system for tighter control in the next trial. We interpret the findings in terms of a conflict-induced switch from metacontrol flexibility to metacontrol persistence, accompanied or even implemented by a reduction of cortical noise.

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

Adolescents rats engage the orbitofrontal-striatal pathway differently than adults during impulsive actions

Adolescence is characterized by increased impulsive and risk-taking behaviors. To better understand the neural networks that subserves impulsivity in adolescents, we used a reward-guided behavioral model that quantifies age differences in impulsive actions in adult and adolescent rats of both sexes. Using chemogenetics, we identified orbitofrontal cortex (OFC) projections to the dorsomedial striatum (DMS) as a critical pathway for age-related execution of impulsive actions. Simultaneous recording of single units and local field potentials in the OFC and DMS during task performance revealed an overall muted response in adolescents during impulsive actions as well as age-specific differences in theta power and OFC-DMS functional connectivity. Collectively, these data reveal that the OFC-DMS pathway is critical for age-differences in reward-guided impulsive actions and provide a network mechanism to enhance our understanding of how adolescent and adult brains coordinate behavioral inhibition.

Optical coherence tomography as a potential surrogate marker of dopaminergic modulation across the life span

The retina has been considered a "window to the brain" and shares similar innervation by the dopaminergic system with the cortex in terms of an unequal distribution of D1 and D2 receptors. Here, we provide a comprehensive overview that Optical Coherence Tomography (OCT), a non-invasive imaging technique, which provides an "in vivo" representation of the retina, shows promise to be used as a surrogate marker of dopaminergic neuromodulation in cognition. Overall, most evidence supports reduced retinal thickness in individuals with dopaminergic dysregulation (e.g., patients with Parkinson's Disease, non-demented older adults) and with poor cognitive functioning. By using the theoretical framework of metacontrol, we derive hypotheses that retinal thinning associated to decreased dopamine (DA) levels affecting D1 families, might lead to a decrease in the signal-to-noise ratio (SNR) affecting cognitive persistence (depending on D1-modulated DA activity) but not cognitive flexibility (depending on D2-modulated DA activity). We argue that the use of OCT parameters might not only be an insightful for cognitive neuroscience research, but also a potentially effective tool for individualized medicine with a focus on cognition. As our society progressively ages in the forthcoming years and decades, the preservation of cognitive abilities and promoting healthy aging will hold of crucial significance. OCT has the potential to function as a swift, non-invasive, and economical method for promptly recognizing individuals with a heightened vulnerability to cognitive deterioration throughout all stages of life.

Interindividual aperiodic resting-state EEG activity predicts cognitive-control styles

The ability to find the right balance between more persistent and more flexible cognitive-control styles is known as "metacontrol." Recent findings suggest a relevance of aperiodic EEG activity and task conditions that are likely to elicit a specific metacontrol style. Here we investigated whether individual differences in aperiodic EEG activity obtained off-task (during resting state) predict individual cognitive-control styles under task conditions that pose different demands on metacontrol. We analyzed EEG resting-state data, task-EEG, and behavioral outcomes from a sample of N = 65 healthy participants performing a Go/Nogo task. We examined aperiodic activity as indicator of "neural noise" in the EEG power spectrum, and participants were assigned to a high-noise or low-noise group according to a median split of the exponents obtained for resting state. We found that off-task aperiodic exponents predicted different cognitive-control styles in Go and Nogo conditions: Overall, aperiodic exponents were higher (i.e., noise was lower) in the low-noise group, who however showed no difference between Go and Nogo trials, whereas the high-noise group exhibited significant noise reduction in the more persistence-heavy Nogo condition. This suggests that trait-like biases determine the default cognitive-control style, which however can be overwritten or compensated for under challenging task demands. We suggest that aperiodic activity in EEG signals represents valid indicators of highly dynamic arbitration between metacontrol styles, representing the brain's capability to reorganize itself and adapt its neural activity patterns to changing environmental conditions.

EEG spectral slope: A reliable indicator for continuous evaluation of consciousness levels during propofol anesthesia

The level of consciousness undergoes continuous alterations during anesthesia. Prior to the onset of propofol-induced complete unconsciousness, degraded levels of behavioral responsiveness can be observed. However, a reliable index to monitor altered consciousness levels during anesthesia has not been sufficiently investigated. In this study, we obtained 60-channel EEG data from 24 healthy participants during an ultra-slow propofol infusion protocol starting with an initial concentration of 1 μg/ml and a stepwise increase of 0.2 μg/ml in concentration. Consecutive auditory stimuli were delivered every 5 to 6 s, and the response time to the stimuli was used to assess the responsiveness levels. We calculated the spectral slope in a time-resolved manner by extracting 5-second EEG segments at each auditory stimulus and estimated their correlation with the corresponding response time. Our results demonstrated that during slow propofol infusion, the response time to external stimuli increased, while the EEG spectral slope, fitted at 15-45 Hz, became steeper, and a significant negative correlation was observed between them. Moreover, the spectral slope further steepened at deeper anesthetic levels and became flatter during anesthesia recovery. We verified these findings using an external dataset. Additionally, we found that the spectral slope of frontal electrodes over the prefrontal lobe had the best performance in predicting the response time. Overall, this study used a time-resolved analysis to suggest that the EEG spectral slope could reliably track continuously altered consciousness levels during propofol anesthesia. Furthermore, the frontal spectral slope may be a promising index for clinical monitoring of anesthesia depth.

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the "neural noise" hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance.SIGNIFICANCE STATEMENT Human cognitive performance is defined not just by the long-run average, but trial-to-trial variability around that average. However, investigations of cognitive abilities and changes during aging have largely ignored this variability component of behavior. We provide evidence that white matter (WM) microstructure predicts individual differences in mean performance and variability in a sample spanning the adult lifespan (18-102). Unlike prior studies of cognitive performance and variability, we modeled variability directly and distinct from mean performance using a dynamic structural equation model, which allows us to decouple variability from mean performance and other complex features of performance (e.g., autoregression). The effects of WM were robust above the effect of age, highlighting the role of WM in promoting fast and consistent performance.

Resting-state BOLD signal variability is associated with individual differences in metacontrol

Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.

Rapid adaptation of predictive models during language comprehension: Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating

Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal (“speaker-based surprisal”) as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual.

The metacontrol hypothesis as diagnostic framework of OCD and ADHD: A dimensional approach based on shared neurobiological vulnerability

Obsessive-compulsive disorder (OCD) and attention-deficit hyperactivity disorder (ADHD) are multi-faceted neuropsychiatric conditions that in many aspects appear to be each other's antipodes. We suggest a dimensional approach, according to which these partially opposing disorders fall onto a continuum that reflects variability regarding alterations of cortico-striato-thalamo-cortical (CSTC) circuits and of the processing of neural noise during cognition. By using theoretical accounts of human cognitive metacontrol, we develop a framework according to which OCD can be characterized by a chronic bias towards exaggerated cognitive persistence, equivalent to a high signal-to-noise ratio (SNR)-which facilitates perseverative behaviour but impairs mental flexibility. In contrast, ADHD is characterized by a chronic bias towards inflated cognitive flexibility, equivalent to a low SNR-which increases behavioural variability but impairs the focusing on one goal and on relevant information. We argue that, when pharmacology is not feasible, novel treatments of these disorders may involve methods to manipulate the signal-to-noise ratio via non-invasive brain stimulation techniques, in order to normalize the situational imbalance between cognitive persistence and cognitive flexibility.

Focusing on cognitive potential as the bright side of mental atypicality

Standard accounts of mental health are based on a "deficit view" solely focusing on cognitive impairments associated with psychiatric conditions. Based on the principle of neural competition, we suggest an alternative. Rather than focusing on deficits, we should focus on the cognitive potential that selective dysfunctions might bring with them. Our approach is based on two steps: the identification of the potential (i.e., of neural systems that might have benefited from reduced competition) and the development of corresponding training methods, using the testing-the-limits approach. Counterintuitively, we suggest to train not only the impaired function but on the function that might have benefitted or that may benefit from the lesser neural competition of the dysfunctional system.

Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: A combined EEG-fMRI study

The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.

Increased scale-free and aperiodic neural activity during sensorimotor integration-a novel facet in Tourette syndrome

Tourette syndrome is a common neurodevelopmental disorder defined by multiple motor and phonic tics. Tics in Tourette syndrome resemble spontaneously occurring movements in healthy controls and are therefore sometimes difficult to distinguish from these. Tics may in fact be mis-interpreted as a meaningful action, i.e. a signal with social content, whereas they lack such information and could be conceived a surplus of action or 'motor noise'. These and other considerations have led to a 'neural noise account' of Tourette syndrome suggesting that the processing of neural noise and adaptation of the signal-to-noise ratio during information processing is relevant for the understanding of Tourette syndrome. So far, there is no direct evidence for this. Here, we tested the 'neural noise account' examining 1/f noise, also called scale-free neural activity as well as aperiodic activity, in n = 74 children, adolescents and adults with Tourette syndrome and n = 74 healthy controls during task performance using EEG data recorded during a sensorimotor integration task. In keeping with results of a previous study in adults with Tourette syndrome, behavioural data confirmed that sensorimotor integration was also stronger in this larger Tourette syndrome cohort underscoring the relevance of perceptual-action processes in this disorder. More importantly, we show that 1/f noise and aperiodic activity during sensorimotor processing is increased in patients with Tourette syndrome supporting the 'neural noise account'. This implies that asynchronous/aperiodic neural activity during sensorimotor integration is stronger in patients with Tourette syndrome compared to healthy controls, which is probably related to abnormalities of GABAergic and dopaminergic transmission in these patients. Differences in 1/f noise and aperiodic activity between patients with Tourette syndrome and healthy controls were driven by high-frequency oscillations and not lower-frequency activity currently discussed to be important in the pathophysiology of tics. This and the fact that Bayesian statistics showed that there is evidence for the absence of a correlation between neural noise and clinical measures of tics, suggest that increased 1/f noise and aperiodic activity are not directly related to tics but rather represents a novel facet of Tourette syndrome.

Effect of acute alcohol intoxication on scale-free neural activity in the lateral septum in rats

Electrophysiological recordings of brain activity show both oscillatory dynamics that typically are analyzed in the time-frequency domain to describe brain oscillatory phenomena and scale-free arrhythmic activity defined as neural noise. Recent studies consider this arrhythmic fractal dynamics of neural noise as a sensitive biomarker of a number of cognitive processes, activity of neurotransmitter systems, changes that accompany neurodegenerative and psychiatric disorders including alcohol use disorder. We tested the changes in neural noise induced by acute alcohol intoxication in the lateral septum for the entire spectrum (1–200 Hz) of local field potential signal and for frequency specific ranges (delta, theta, beta, gamma and epsilon bands). Five male Wistar rats were implanted with intracranial electrodes and local field potential signal was measured for baseline activity and activity induced by acute ethanol intoxication (2 g/kg). Change in neural noise dynamics was assessed as a change in the slope of linear regression fit of power spectral density curves in double logarithmic scale. In our study alcohol resulted in lower incline of scale-free activity in the lateral septum for high frequency range and for the whole spectrum, which is interpreted generally as increase in neural noise and change in neuronal processing in a more stochastic way initiated by the acute alcohol intoxication. At the same time, we observed decrease in neural noise for low frequency range. The observed changes may be related to the shift of the excitatory-inhibitory balance towards inhibition and changes in neurotransmission mostly in the GABAergic system. Scale-free activity was sensitive in the conditions of acute alcohol intoxication, therefore to understand its role in alcohol use disorder we need more data and studies on the underlying processes. Future studies should include simultaneous recordings and analysis of arrhythmic dynamics with the oscillatory and multiunit spiking activity in the lateral septum. It can reveal the contribution of different-scale processes in changes driven by acute alcohol intoxication and clarify the specific electrophysiological mechanisms.

A neural noise account of Gilles de la Tourette syndrome

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A neural noise account on Tourette syndrome is conceptualized.

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We outline how neurophysiological methods can be used to test this account.

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The neural noise account may lead to novel treatment options.

Tics, often preceded by premonitory urges, are the clinical hallmark of Tourette syndrome. They resemble spontaneous movements, but are exaggerated, repetitive and appear misplaced in a given communication context. Given that tics often go unnoticed, it has been suggested that they represent a surplus of action, or motor noise. In this conceptual position paper, we propose that tics and urges, but also patterns of the cognitive profile in Tourette syndrome might be explained by the principle of processing of neural noise and adaptation to it during information processing. We review evidence for this notion in the light of Tourette pathophysiology and outline why neurophysiological and imaging approaches are central to examine a possibly novel view on Tourette syndrome. We discuss how neurophysiological data at multiple levels of inspections, i.e., from local field potentials using intra-cranial recording to scalp-measured EEG data, in combination with imaging approaches, can be used to examine the neural noise account in Tourette syndrome. We outline what signal processing methods may be suitable for that. We argue that, as a starting point, the analysis of 1/f neural noise or scale-free activity may be suitable to investigate the role of neural noise and its adaptation during information processing in Tourette syndrome. We outline, how the neural noise perspective, if substantiated by further neurophysiological studies and re-analyses of existing data, may pave the way to novel interventions directly targeting neural noise levels and patterns in Tourette syndrome.

Spontaneous Alpha and Theta Oscillations Are Related to Complementary Aspects of Cognitive Control in Younger and Older Adults

The resting-state human electroencephalogram (EEG) power spectrum is dominated by alpha (8-12 Hz) and theta (4-8 Hz) oscillations, and also includes non-oscillatory broadband activity inversely related to frequency (1/f activity). Gratton proposed that alpha and theta oscillations are both related to cognitive control function, though in a complementary manner. Alpha activity is hypothesized to facilitate the maintenance of representations, such as task sets in preparation for expected task conditions. In contrast, theta activity would facilitate changes in representations, such as the updating of task sets in response to unpredicted task demands. Therefore, theta should be related to reactive control (which may prompt changes in task representations), while alpha may be more relevant to proactive control (which implies the maintenance of current task representations). Less is known about the possible relationship between 1/f activity and cognitive control, which was analyzed here in an exploratory fashion. To investigate these hypothesized relationships, we recorded eyes-open and eyes-closed resting-state EEG from younger and older adults and subsequently tested their performance on a cued flanker task, expected to elicit both proactive and reactive control processes. Results showed that alpha power and 1/f offset were smaller in older than younger adults, whereas theta power did not show age-related reductions. Resting alpha power and 1/f offset were associated with proactive control processes, whereas theta power was related to reactive control as measured by the cued flanker task. All associations were present over and above the effect of age, suggesting that these resting-state EEG correlates could be indicative of trait-like individual differences in cognitive control performance, which may be already evident in younger adults, and are still similarly present in healthy older adults.

Characterizing pink and white noise in the human electroencephalogram

OBJECTIVE

The power spectrum of the human electroencephalogram (EEG) as a function of frequency is a mix of brain oscillations (e.g. alpha activity around 10 Hz) and non-oscillations or noise of uncertain origin. "White noise" is uniformly distributed over frequency, while "pink noise" has an inverse power-frequency relation (power ∝ 1/f). Interest in EEG pink noise has been growing, but previous human estimates appear methodologically flawed. We propose a new approach to extract separate valid estimates of pink and white noise from an EEG power spectrum.

APPROACH

We use simulated data to demonstrate its effectiveness compared with established procedures, and provide an illustrative example from a new resting eyes-open (EO) and eyes-closed (EC) dataset. The topographic characteristics of the obtained pink and white noise estimates are examined, as is the alpha power in this sample.

MAIN RESULTS

Valid pink and white noise estimates were successfully obtained for each of our 5400 individual spectra (60 participants × 30 electrodes × 3 conditions/blocks [EO1, EC, EO2]). The 1/f noise had a distinct central scalp topography, and white noise was occipital in distribution, both differing from the parietal topography of the alpha oscillation. These differences point to their separate neural origins. EC pink and white noise powers were globally greater than in EO.

SIGNIFICANCE

This valid estimation of pink and white noise in the human EEG holds promise for more accurate assessment of oscillatory neural activity in both typical and clinical groups, such as those with attention deficits. Further, outside the human EEG, the new methodology can be generalized to remove noise from spectra in many fields of science and technology.

Cognitive profile in Restless Legs Syndrome: A signal-to-noise ratio account

Restless legs syndrome (RLS) is a common neurological disorder characterized by a sensorimotor condition, where patients feel an uncontrollable urge to move the lower limbs in the evening and/or during the night. RLS does not only have a profound impact on quality of life due to the disturbed night-time sleep, but there is growing evidence that untreated or insufficiently managed RLS might also cause cognitive changes in patients affected by this syndrome. It has been proposed that RLS is caused by alterations in the signal-to-noise ratio (SNR) and in dopamine (DA) neurotransmission in the nervous system. Based on this evidence, we propose the “SNR-DA hypothesis” as an explanation of how RLS could affect cognitive performance. According to this hypothesis, variations/reductions in the SNR underlie RLS-associated cognitive deficits, which follow an inverted U-shaped function: In unmedicated patients, low dopamine levels worsen the SNR, which eventually impairs cognition. Pharmacological treatment enhances DA levels in medicated patients, which likely improves/normalizes the SNR in case of optimal doses, thus restoring cognition to a normal level. However, overmedication might push patients past the optimal point on the inverted U-shaped curve, where an exaggerated SNR potentially impairs cognitive performance relying on cortical noise such as cognitive flexibility. Based on these assumptions of SNR alterations, we propose to directly measure neural noise via “1/f noise” and related metrics to use transcranial random noise stimulation (tRNS), a noninvasive brain stimulation method which manipulates the SNR, as a research tool and potential treatment option for RLS.

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Restless legs syndrome (RLS) is a common neurological disorder.

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RLS is caused by alterations in the SNR ratio and in DA neurotransmission.

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The SNR- DA hypothesis how RLS affects cognitive performance is presented.

High-dose ethanol intoxication decreases 1/f neural noise or scale-free neural activity in the resting state

Binge drinking is a frequent phenomenon in many western societies and has been associated with an increased risk of developing alcohol use disorder later in life. Yet, the effects of high-dose alcohol intoxication on neurophysiological processes are still quite poorly understood. This is particularly the case given that neurophysiological brain activity not only contains recurring (oscillatory) patterns of activity, but also a significant fraction of "scale-free" or arrhythmic dynamics referred to as 1/f type activity, pink noise, or 1/f neural noise. Neurobiological considerations suggest that it should be modulated by alcohol intoxication. To investigate this assumption, we collected resting state EEG data from n = 23 healthy young male subjects in a crossover design, where each subject was once tested sober and once tested while intoxicated (mean breath alcohol concentration of 1.1 permille ±0.2). Analyses of the 1/f neural dynamics showed that ethanol intoxication decreased resting state 1/f neural noise, as compared with a sober state. The effects were strongest when the eyes were closed and particularly reliable in the beta frequency band. Given that the dynamics of the beta band have been shown to strongly depend on GABA_A receptor neural transmission, this finding nicely aligns with the fact that ethanol increases GABAergic signaling. The study reveals a currently unreported effect of binge drinking on neurophysiological dynamics, which likely revealed a higher sensitivity for ethanol effects than most commonly considered measures of power in neural oscillations. Implications and applicability of these findings are discussed.

Shifts in broadband power and alpha peak frequency observed during long-term isolation

Prolonged periods of social isolation and spatial confinement do not only represent an issue that needs to be faced by a few astronauts during space missions, but can affect all of us as recently shown during pandemic situations. The fundamental question, how the brain adapts to periods of sensory deprivation and re-adapts to normality, has only received little attention. Here, we use eyes closed and eyes open resting-state electroencephalographic (EEG) recordings to investigate how neural activity is altered during 120 days of isolation in a spatially confined, space-analogue environment. After disentangling oscillatory patterns from 1/f activity, we show that isolation leads to a reduction in broadband power and a flattening of the 1/f spectral slope. Beyond that, we observed a reduction in alpha peak frequency during isolation, but did not find strong evidence for isolation-induced changes that are of oscillatory nature. Critically, all effects reversed upon release from isolation. These findings suggest that isolation and concomitant sensory deprivation lead to an enhanced cortical deactivation which might be explained by a reduction in the mean neuronal population firing rate.

The Downsides of Cognitive Enhancement

Cognitive enhancement is becoming progressively popular as a subject of scientific investigation and by the public, although possible adverse effects are not sufficiently understood. We call for cognitive enhancement to build on more specific, mechanistic theories given that a-theoretical approaches to cognitive enhancement are both a cause and a consequence of a strong, if not exclusive focus on the benefits of procedures suited to enhance human cognition. We focus on downsides of cognitive enhancement and suggest that every attempt to enhance human cognition needs to deal with two basic principles: the neuro-competition principle and the nonlinearity principle. We discuss the possibility of both principles in light of recent attempts to improve human cognition by means of transcranial direct current stimulation, a well-established brain stimulation method, and clinically relevant nootropic drugs. We propose that much stronger emphasis on mechanistic theorizing is necessary in guiding future research on both the upsides and the downsides of cognitive enhancement.

A literature review on the neurophysiological underpinnings and cognitive effects of transcutaneous vagus nerve stimulation: challenges and future directions

Brain stimulation approaches are important to gain causal mechanistic insights into the relevance of functional brain regions and/or neurophysiological systems for human cognitive functions. In recent years, transcutaneous vagus nerve stimulation (tVNS) has attracted considerable popularity. It is a noninvasive brain stimulation technique based on the stimulation of the vagus nerve. The stimulation of this nerve activates subcortical nuclei, such as the locus coeruleus and the nucleus of the solitary tract, and from there, the activation propagates to the cortex. Since tVNS is a novel stimulation technique, this literature review outlines a brief historical background of tVNS, before detailing underlying neurophysiological mechanisms of action, stimulation parameters, cognitive effects of tVNS on healthy humans, and, lastly, current challenges and future directions of tVNS research in cognitive functions. Although more research is needed, we conclude that tVNS, by increasing norepineprine (NE) and gamma-aminobutyric acid (GABA) levels, affects NE- and GABA-related cognitive performance. The review provides detailed background information how to use tVNS as a neuromodulatory tool in cognitive neuroscience and outlines important future leads of research on tVNS.

Passive perceptual learning modulates motor inhibitory control in superior frontal regions

Response inhibition is of vital importance in the context of controlling inappropriate responses. The role of perceptual processes during inhibitory control has attracted increased interest. Yet, we are far from an understanding of the mechanisms. One candidate mechanism by which perceptual processes may affect response inhibition refers to “gain control” that is closely linked to the signal‐to‐noise ratio of incoming information. A means to modulate the signal‐to‐noise ratio and gain control mechanisms is perceptual learning. In the current study, we examine the impact of perceptual learning (i.e., passive repetitive sensory stimulation) on response inhibition combining EEG signal decomposition with source localization analyses. A tactile GO/NOGO paradigm was conducted to measure action restraint as one subcomponent of response inhibition. We show that passive perceptual learning modulates response inhibition processes. In particular, perceptual learning attenuates the detrimental effect of response automation during inhibitory control. Temporally decomposed EEG data show that stimulus‐related and not response selection processes during conflict monitoring are linked to these effects. The superior and middle frontal gyrus (BA6), as well as the motor cortex (BA4), are associated with the effects of perceptual learning on response inhibition. Reliable neurophysiological effects were not evident on the basis of standard ERPs, which has important methodological implications for perceptual learning research. The results detail how lower level sensory plasticity protocols affect higher‐order cognitive control functions in frontal cortical structures.

The Modulation of Neural Noise Underlies the Effectiveness of Methylphenidate Treatment in Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Various lines of research suggest that the stability of neural processes is low in attention-deficit/hyperactivity disorder (ADHD). Considering overarching neural principles, this lack of stability relates to increased levels of neural noise. However, no study has directly examined neural noise in ADHD. Likewise, it is unknown whether the modulation of neural noise reflects a mechanistic link as to why methylphenidate (MPH) is effective in treating impulsivity in ADHD.

METHODS

We compared neural noise between 29 juvenile patients with ADHD and 32 healthy control subjects and examined the effects of MPH. We examined 1/f neural noise of electroencephalogram data collected while participants performed a response inhibition (Go/NoGo) task.

RESULTS

Specific during NoGo trials, children with ADHD showed more neural noise than healthy control subjects. This was especially the case with regard to the theta frequency band, which is very closely related to cognitive control. MPH treatment reduced neural noise in ADHD to the level of healthy control subjects. Correlational analyses showed a direct relationship between decreases in neural noise and increases in behavioral performance. Mechanistically, this can be explained by the MPH-induced increase in dopaminergic neurotransmission that enhances the signal-to-noise ratio in neural networks and thus reduces neural noise.

CONCLUSIONS

This study is the first to demonstrate increased (pink) neural noise in patients with ADHD and its reduction through MPH treatment. The study reveals an important mechanistic link as to why MPH is effective in treating impulsivity in ADHD.