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Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signals in non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging. [ABSTRACT FROM AUTHOR]

Elevated "neural noise" has been advanced as an explanation of autism and autistic sensory experiences. However, functional neuroimaging measures of neural noise may be vulnerable to contamination by recording noise. This study explored variability of electrophysiological responses to tones of different intensities in 127 autistic and 79 typically-developing children aged 2-5 years old. A rigorous data processing pipeline, including advanced visualizations of different signal sources that were maximally independent across different time lags, was used to identify and eliminate putative recording noise. Inter-trial variability was measured using median absolute deviations (MADs) of EEG amplitudes across trials and inter-trial phase coherence (ITPC). ITPC was elevated in autism in the 50 and 60 dB intensity conditions, suggesting diminished (rather than elevated) neural noise in autism, although reduced ITPC to soft 50 dB sounds was associated with increased loudness discomfort. Autistic and non-autistic participants did not differ in MADs, and indeed, the vast majority of the statistical tests examined in this study yielded no significant effects. These results appear inconsistent with the neural noise account.

Abstract “Metacontrol” refers to the ability to find the right balance between more persistent and more flexible cognitive control styles, depending on task demands. Recent research on tasks involving response conflict regulation indicates a consistent link between aperiodic EEG activity and task conditions that demand a more or less persistent control style. In this study, we explored whether this connection between metacontrol and aperiodic activity also applies to cognitive flexibility. We examined EEG and behavioral data from two separate samples engaged in a task-switching paradigm, allowing for an internal replication of our findings. Both studies revealed that aperiodic activity significantly decreased during task switching compared to task repetition. Our results support the predictions of metacontrol theory but contradict those of traditional control theories which would have predicted the opposite pattern of results. We propose that aperiodic activity observed in EEG signals serves as a valid indicator of dynamic neuroplasticity in metacontrol, suggesting that truly adaptive metacontrol does not necessarily bias processing towards persistence in response to every control challenge, but chooses between persistence and flexibility biases depending on the nature of the challenge.

“Metacontrol” refers to the ability to find the right balance between more persistent and more flexible cognitive control styles, depending on task demands. Recent research on tasks involving response conflict regulation indicates a consistent link between aperiodic EEG activity and task conditions that demand a more or less persistent control style. In this study, we explored whether this connection between metacontrol and aperiodic activity also applies to cognitive flexibility. We examined EEG and behavioral data from two separate samples engaged in a task-switching paradigm, allowing for an internal replication of our findings. Both studies revealed that aperiodic activity significantly decreased during task switching compared to task repetition. Our results support the predictions of metacontrol theory but contradict those of traditional control theories which would have predicted the opposite pattern of results. We propose that aperiodic activity observed in EEG signals serves as a valid indicator of dynamic neuroplasticity in metacontrol, suggesting that truly adaptive metacontrol does not necessarily bias processing towards persistence in response to every control challenge, but chooses between persistence and flexibility biases depending on the nature of the challenge. [ABSTRACT FROM AUTHOR]

The human brain exhibits both oscillatory and aperiodic, or 1/f, activity. Although a large body of research has focused on the relationship between brain rhythms and sensory processes, aperiodic activity has often been overlooked as functionally irrelevant. Prompted by recent findings linking aperiodic activity to the balance between neural excitation and inhibition, we investigated its effects on the temporal resolution of perception. We recorded electroencephalography (EEG) from participants (both sexes) during the resting state and a task in which they detected the presence of two flashes separated by variable interstimulus intervals. Two-flash discrimination accuracy typically follows a sigmoid function whose steepness reflects perceptual variability or inconsistent integration/segregation of the stimuli. We found that individual differences in the steepness of the psychometric function correlated with EEG aperiodic exponents over posterior scalp sites. In other words, participants with flatter EEG spectra (i.e., greater neural excitation) exhibited increased sensory noise, resulting in shallower psychometric curves. Our finding suggests that aperiodic EEG is linked to sensory integration processes usually attributed to the rhythmic inhibition of neural oscillations. Overall, this correspondence between aperiodic neural excitation and behavioral measures of sensory noise provides a more comprehensive explanation of the relationship between brain activity and sensory integration and represents an important extension to theories of how the brain samples sensory input over time. [ABSTRACT FROM AUTHOR]

The beneficial psychological effects of exercise might be explained by self-determination theory and autonomy. However, the underlying neurophysiological mechanisms are even less elucidated. Previously neglected, aperiodic (1/f) brain activity is suggested to indicate enhanced cortical inhibition when the slope is steeper. This is thought to be associated with an increased cognitive performance. Therefore, we hypothesize that running with a self-selected intensity and thus given autonomy leads to stronger neural inhibition accompanied by psychological improvements. Twenty-nine runners performed two 30-min runs. First, they chose their individual feel-good intensity (self-selected run; SR). After a 4-weeks washout, the same speed was blindly prescribed (imposed run; IR). Acute effects on mood (Feeling Scale, Felt Arousal Scale, MoodMeter®), cognition (d2-R, digit span test) and electrocortical activity (slope, offset, 1/f-corrected alpha and low beta band) were analyzed before and after the runs. Both runs had an equal physical workload and improved mood in the Felt Arousal Scale, but not in the Feeling Scale or MoodMeter®. Cognitive performance improved after both runs in the d2-R, while it remained stable in the digit span test after SR, but decreased after IR. After running, the aperiodic slope was steeper, and the offset was reduced. Alpha activity increased after SR only, while low beta activity decreased after both conditions. The aperiodic features partially correlated with mood and cognition. SR was not clearly superior regarding psychological effects. Reduced aperiodic brain activity indicates enhanced neural inhibition after both runs. The 1/f-corrected alpha band may emphasize a different neural processing between both runs. [ABSTRACT FROM AUTHOR]

Temporal processing deficits in Developmental Dyslexia (DD) have been documented extensively at the behavioral level, leading to the formulation of neural theories positing that such anomalies in parsing multisensory input rely on aberrant synchronization of neural oscillations or to an excessive level of neural noise. Despite reading being primarily supported by visual functions, experimental evidence supporting these theories remains scarce. Here, we tested 26 adults with DD (9 females) and 31 neurotypical controls (16 females) with a temporal segregation/integration task that required participants to either integrate or segregate two rapidly presented displays while their EEG activity was recorded. We confirmed a temporal sampling deficit in DD, which specifically affected the rapid segregation of visual input. While the ongoing alpha frequency and the excitation/inhibition (E/I) ratio (i.e., an index of neural noise quantified by the aperiodic exponent) were differently modulated based on task demands in typical readers, DD participants exhibited an impairment in alpha speed modulation and an altered E/I ratio that affected their rapid visual sampling. Nonetheless, an association between visual temporal sampling accuracy and both alpha frequency and the E/I ratio measured at rest were evident in the DD group, further confirming an anomalous interplay between alpha synchronization, the E/I ratio and active visual sampling. These results provide evidence that both trait- and state-like differences in alpha-band synchronization and neural noise levels coexist in the dyslexic brain and are synergistically responsible for cascade effects on visual sampling and reading.

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. Via the FOOOF toolbox which calculates aperiodic exponents as indicators of "neural noise," we were able to theoretically link aperiodic EEG activity to cognitive‐control styles. We found that in demanding conditions, the modulation of aperiodic activity is more driven by stimuli and demand‐signaling information than by trait‐like biases. [ABSTRACT FROM AUTHOR]

This study aimed to elucidate the mechanisms underlying the speed-accuracy relationship in a tennis topspin single-handed forehand groundstroke. Groundstrokes at three different speeds by nine college players were captured, with the variability of the ball landing position evaluated as indices of accuracy. Variabilities of ball launch variables (speed, launch angle, spin, etc.) and conversion ratios from these variabilities to the variability of the ball landing position were quantified. These variabilities and their conversion ratios could be influenced by different efforts exerted to generate ball speeds and different ball trajectories required to hit a target at each speed, respectively. The speed-accuracy trade-off was observed only in the hitting direction. While the variability of the spin axis increased, it had minimal influence on the ball landing position. Conversely, the conversion ratio in the hitting direction of the velocity elevation angle significantly increased, while its variability remained unchanged. Consequently, the geometrical requirements of ball trajectories can be responsible for the speed-accuracy trade-off. Therefore, even skilled players capable of maintaining consistent ball launch variables regardless of the shot speed should deliberately choose a moderately slower ball speed to avoid an inevitable increase in the variability of the ball landing position due to geometrical requirements. [ABSTRACT FROM AUTHOR]

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes. [ABSTRACT FROM AUTHOR]

Tinnitus is a sound heard by 15% of the general population in the absence of any external sound. Because external sounds can sometimes mask tinnitus, tinnitus is assumed to affect the perception of external sounds, leading to hypotheses such as "tinnitus filling in the temporal gap" in animal models and "tinnitus inducing hearing difficulty" in human subjects. Here we compared performance in temporal, spectral, intensive, masking and speech-in-noise perception tasks between 45 human listeners with chronic tinnitus (18 females and 27 males with a range of ages and degrees of hearing loss) and 27 young, normal-hearing listeners without tinnitus (11 females and 16 males). After controlling for age, hearing loss, and stimulus variables, we discovered that, contradictory to the widely held assumption, tinnitus does not interfere with the perception of external sounds in 32 of the 36 measures. We interpret the present result to reflect a bottom-up pathway for the external sound and a separate top-down pathway for tinnitus. We propose that these two perceptual pathways can be independently modulated by attention, which leads to the asymmetrical interaction between external and internal sounds, and several other puzzling tinnitus phenomena such as discrepancy in loudness between tinnitus rating and matching. The present results suggest not only a need for new theories involving attention and central noise in animal tinnitus models but also a shift in focus from treating tinnitus to managing its comorbid conditions when addressing complaints about hearing difficulty in individuals with tinnitus.SIGNIFICANCE STATEMENT Tinnitus, or ringing in the ears, is a neurologic disorder that affects 15% of the general population. Here we discovered an asymmetrical relationship between tinnitus and external sounds: although external sounds have been widely used to cover up tinnitus, tinnitus does not impair, and sometimes even improves, the perception of external sounds. This counterintuitive discovery contradicts the general belief held by scientists, clinicians, and even individuals with tinnitus themselves, who often report hearing difficulty, especially in noise. We attribute the counterintuitive discovery to two independent pathways: the bottom-up perception of external sounds and the top-down perception of tinnitus. Clinically, the present work suggests a shift in focus from treating tinnitus itself to treating its comorbid conditions and secondary effects.

While noise is generally believed to impair performance, the detection of weak stimuli can sometimes be enhanced by introducing optimum noise levels. This phenomenon is termed 'Stochastic Resonance' (SR). Past evidence suggests that autistic individuals exhibit higher neural noise than neurotypical individuals. It has been proposed that the enhanced performance in Autism Spectrum Disorder (ASD) on some tasks could be due to SR. Here we present a computational model, lab-based, and online visual identification experiments to find corroborating evidence for this hypothesis in individuals without a formal ASD diagnosis. Our modeling predicts that artificially increasing noise results in SR for individuals with low internal noise (e.g., neurotypical), however not for those with higher internal noise (e.g., autistic, or neurotypical individuals with higher autistic traits). It also predicts that at low stimulus noise, individuals with higher internal noise outperform those with lower internal noise. We tested these predictions using visual identification tasks among participants from the general population with autistic traits measured by the Autism-Spectrum Quotient (AQ). While all participants showed SR in the lab-based experiment, this did not support our model strongly. In the online experiment, significant SR was not found, however participants with higher AQ scores outperformed those with lower AQ scores at low stimulus noise levels, which is consistent with our modeling. In conclusion, our study is the first to investigate the link between SR and superior performance by those with ASD-related traits, and reports limited evidence to support the high neural noise/SR hypothesis. [ABSTRACT FROM AUTHOR]

Introduction: Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods: Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results: Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion: These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment. [ABSTRACT FROM AUTHOR]

While noise is generally believed to impair performance, the detection of weak stimuli can sometimes be enhanced by introducing optimum noise levels. This phenomenon is termed ‘Stochastic Resonance’ (SR). Past evidence suggests that autistic individuals exhibit higher neural noise than neurotypical individuals. It has been proposed that the enhanced performance in Autism Spectrum Disorder (ASD) on some tasks could be due to SR. Here we present a computational model, lab-based, and online visual identification experiments to find corroborating evidence for this hypothesis in individuals without a formal ASD diagnosis. Our modeling predicts that artificially increasing noise results in SR for individuals with low internal noise (e.g., neurotypical), however not for those with higher internal noise (e.g., autistic, or neurotypical individuals with higher autistic traits). It also predicts that at low stimulus noise, individuals with higher internal noise outperform those with lower internal noise. We tested these predictions using visual identification tasks among participants from the general population with autistic traits measured by the Autism-Spectrum Quotient (AQ). While all participants showed SR in the lab-based experiment, this did not support our model strongly. In the online experiment, significant SR was not found, however participants with higher AQ scores outperformed those with lower AQ scores at low stimulus noise levels, which is consistent with our modeling. In conclusion, our study is the first to investigate the link between SR and superior performance by those with ASD-related traits, and reports limited evidence to support the high neural noise/SR hypothesis.

Prediction of upcoming words during reading has been suggested to enhance the efficiency of discourse processing. Emerging models have postulated that predictive mechanisms require synchronous firing of neural networks, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity - and thereby synchronous neuronal firing - is 1/f neural noise extracted from EEG spectral power. Previous research has indicated that this measure of 1/f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/f neural noise and whether this measure predicted ERP correlates of successful lexical prediction during discourse comprehension. 1/f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/f noise was a significant predictor of N400 effects of successful lexical prediction; however, noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/f noise across research populations.

Objective: Cognitive dysfunction has been observed consistently in a subset of breast cancer survivors. Yet the precise neurophysiological origins of cancer-related cognitive decline remain unknown. The current study assessed neural noise (1/f activity in electroencephalogram [EEG]) in breast cancer survivors as a potential contributor to observed cognitive dysfunction from pre- to post-treatment., Methods: We measured EEG in a longitudinal design during performance of the paired-click task and the revised Attention Network Test (ANT-R) to investigate pre- versus post-treatment effects of neural noise in breast cancer patients (n = 20 in paired click; n = 19 in ANT-R) compared with healthy controls (n = 32 in paired click; n = 29 in ANT-R)., Results: In both paradigms, one sensory (paired click) and one cognitive (ANT-R), we found that neural noise was significantly elevated after treatment in patients, remaining constant from pretest to posttest in controls. In the ANT-R, patients responded more slowly than controls on invalid cuing trials. Increased neural noise was associated with poorer alerting and poorer inhibitory control of attention (as measured by behavioral network scores), particularly for patients after treatment., Conclusions: The current study is the first to show a deleterious effect of breast cancer and/or cancer treatment on neural noise, pointing to alterations in the relative balance of excitatory and inhibitory synaptic inputs, while also suggesting promising approaches for cognitive rehabilitation., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

One hypothesis for why humans enjoy musical rhythms relates to their prediction of when each beat should occur. The ability to predict the timing of an event is important from an evolutionary perspective. Therefore, our brains have evolved internal mechanisms for processing the progression of time. However, due to inherent noise in neural signals, this prediction is not always accurate. Theoretical considerations of optimal estimates suggest the occurrence of certain systematic errors made by the brain when estimating the timing of beats in rhythms. Here, we tested psychophysically whether these systematic errors exist and if so, how they depend on stimulus parameters. Our experimental data revealed two main types of systematic errors. First, observers perceived the time of the last beat of a rhythmic pattern as happening earlier than actual when the inter-beat interval was short. Second, the perceived time of the last beat was later than the actual when the inter-beat interval was long. The magnitude of these systematic errors fell as the number of beats increased. However, with many beats, the errors due to long inter-beat intervals became more apparent. We propose a Bayesian model for these systematic errors. The model fits these data well, allowing us to offer possible explanations for how these errors occurred. For instance, neural processes possibly contributing to the errors include noisy and temporally asymmetric impulse responses, priors preferring certain time intervals, and better-early-than-late loss functions. We finish this article with brief discussions of both the implications of systematic errors for the appreciation of rhythm and the possible compensation by the brain's motor system during a musical performance. [ABSTRACT FROM AUTHOR]

The noisy computation hypothesis of developmental dyslexia (DD) is particularly appealing because it can explain deficits across a variety of domains, such as temporal, auditory, phonological, visual and attentional processes. A key prediction is that noisy computations lead to more variable and less stable word representations. A way to test this hypothesis is through repetition of words, that is, when there is noise in the system, the neural signature of repeated stimuli should be more variable. The hypothesis was tested in an functional magnetic resonance imaging experiment with dyslexic and typical readers by repeating words twelve times. Variability measures were computed both at the behavioral and neural levels. At the behavioral level, we compared the standard deviation of reaction time distributions of repeated words. At the neural level, in addition to standard univariate analyses and measures of intra-item variability, we also used multivariate pattern analyses (representational similarity and classification) to find out whether there was evidence for noisier representations in dyslexic readers compared to typical readers. Results showed that there were no significant differences between the two groups in any of the analyses despite robust results within each group (i.e., high representational similarity between repeated words, good classification of words vs. non-words). In summary, there was no evidence in favor of the idea that dyslexic readers would have noisier neural representations than typical readers. [ABSTRACT FROM AUTHOR]

One hypothesis for why humans enjoy musical rhythms relates to their prediction of when each beat should occur. The ability to predict the timing of an event is important from an evolutionary perspective. Therefore, our brains have evolved internal mechanisms for processing the progression of time. However, due to inherent noise in neural signals, this prediction is not always accurate. Theoretical considerations of optimal estimates suggest the occurrence of certain systematic errors made by the brain when estimating the timing of beats in rhythms. Here, we tested psychophysically whether these systematic errors exist and if so, how they depend on stimulus parameters. Our experimental data revealed two main types of systematic errors. First, observers perceived the time of the last beat of a rhythmic pattern as happening earlier than actual when the inter-beat interval was short. Second, the perceived time of the last beat was later than the actual when the inter-beat interval was long. The magnitude of these systematic errors fell as the number of beats increased. However, with many beats, the errors due to long inter-beat intervals became more apparent. We propose a Bayesian model for these systematic errors. The model fits these data well, allowing us to offer possible explanations for how these errors occurred. For instance, neural processes possibly contributing to the errors include noisy and temporally asymmetric impulse responses, priors preferring certain time intervals, and better-early-than-late loss functions. We finish this article with brief discussions of both the implications of systematic errors for the appreciation of rhythm and the possible compensation by the brain’s motor system during a musical performance.

The noisy computation hypothesis of developmental dyslexia (DD) is particularly appealing because it can explain deficits across a variety of domains, such as temporal, auditory, phonological, visual and attentional processes. A key prediction is that noisy computations lead to more variable and less stable word representations. A way to test this hypothesis is through repetition of words, that is, when there is noise in the system, the neural signature of repeated stimuli should be more variable. The hypothesis was tested in an functional magnetic resonance imaging experiment with dyslexic and typical readers by repeating words twelve times. Variability measures were computed both at the behavioral and neural levels. At the behavioral level, we compared the standard deviation of reaction time distributions of repeated words. At the neural level, in addition to standard univariate analyses and measures of intra-item variability, we also used multivariate pattern analyses (representational similarity and classification) to find out whether there was evidence for noisier representations in dyslexic readers compared to typical readers. Results showed that there were no significant differences between the two groups in any of the analyses despite robust results within each group (i.e., high representational similarity between repeated words, good classification of words vs. non-words). In summary, there was no evidence in favor of the idea that dyslexic readers would have noisier neural representations than typical readers.

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.

Visual evoked potentials (VEPs) provide a means to examine neural mechanisms in autism with high temporal resolution. Conventional VEP analysis relies on subjective inspection of a few points (peaks and troughs) in the time‐domain waveform. The current study applied power spectral analysis and magnitude‐squared coherence (MSC) statistics (frequency‐domain measures) to VEPs recorded during 1‐minute runs and with a recently developed short‐duration technique that allow for objective examination of the responses (Zemon & Gordon, European Journal of Neuroscience, 2018, 48, 1765–1788) from nonautistic and autistic children. Results indicate that, for both groups, early time‐domain measures (P60, N75, P100) are highly correlated with middle‐ and high‐frequency (14–28 and 30–48 Hz, respectively) mechanisms, and late measures are highly correlated with a low‐frequency (6–12 Hz) mechanism. One frequency‐domain measure (power in the middle‐frequency band) is capable of predicting the key amplitude measure (N75‐P100) with high accuracy. MSC and power measures were combined to yield separate measures of signal and noise strength to evaluate alternate hypotheses in autism. Linear mixed‐effects modeling demonstrated selective differences in early time‐domain and middle‐to‐high frequency‐domain measures in autistic children as compared to nonautistic children given both recording techniques, implicating weaker excitatory input to the cortex. Receiver‐operating‐characteristic curve analysis showed predictive diagnostic accuracy for middle‐ and high‐frequency bands based on MSC. These findings support the value of frequency analysis measures (power spectral analysis and MSC) in the objective examination of neural differences in autism. Lay Summary: Visual evoked potentials (VEPs) are used to assess neural mechanisms. Typically, VEPs are analyzed by subjective examination of time‐series waveforms; but here objective techniques were applied to quantify VEP frequency components to investigate neural differences between autistic and nonautistic children. The objective measures demonstrate group differences in brain function that point to weaker excitatory input to the cortex in autism. [ABSTRACT FROM AUTHOR]

Background: Neural information processing is subject to noise and this leads to variability in neural firing and behavior. Schizophrenia has been associated with both more variable motor control and impaired cortical inhibition, which is crucial for excitatory/inhibitory balance in neural commands. Hypothesis: In this study, we hypothesized that impaired intracortical inhibition in motor cortex would contribute to task-related motor noise in schizophrenia. Methods: We measured variability of force and of electromyographic (EMG) activity in upper limb and hand muscles during a visuomotor grip force-tracking paradigm in patients with schizophrenia (N = 25), in unaffected siblings (N = 17) and in healthy control participants (N = 25). Task-dependent primary motor cortex (M1) excitability and inhibition were assessed using transcranial magnetic stimulation (TMS). Results: During force maintenance patients with schizophrenia showed increased variability in force and EMG, despite similar mean force and EMG magnitudes. Compared to healthy controls, patients with schizophrenia also showed increased M1 excitability and reduced cortical inhibition during grip-force tracking. EMG variability and force variability correlated negatively to cortical inhibition in patients with schizophrenia. EMG variability also correlated positively to negative symptoms. Siblings had similar variability and cortical inhibition compared to controls. Increased EMG and force variability indicate enhanced motor noise in schizophrenia, which relates to reduced motor cortex inhibition. Conclusion: The findings suggest that excessive motor noise in schizophrenia may arise from an imbalance of M1 excitation/inhibition of GABAergic origin. Thus, higher motor noise may provide a useful marker of impaired cortical inhibition in schizophrenia.

Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or 'scale-free' activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain. There is a lack of clear understanding of how these different measures relate to each other and to what extent they reflect the same or different properties of the data, which makes it difficult to synthesize results across approaches and complicates our overall understanding of the properties, biological significance, and demographic, clinical, and behavioral correlates of aperiodic neural activity. To address this problem, in this project we systematically survey the different approaches for measuring aperiodic neural activity, starting with an automated literature analysis to curate a collection of the most common methods. We then evaluate and compare these methods, using statistically representative time series simulations. In doing so, we establish consistent relationships between the measures, showing that much of what they capture reflects shared variance - though with some notable idiosyncrasies. Broadly, frequency domain methods are more specific to aperiodic features of the data, whereas time domain measures are more impacted by oscillatory activity. We extend this analysis by applying the measures to a series of empirical EEG and iEEG datasets, replicating the simulation results. We conclude by summarizing the relationships between the multiple methods, emphasizing opportunities for reexamining previous findings and for future work., Competing Interests: Conflicts of Interest The authors declare no competing interests.

There is a widespread attitude in epistemology that, if you know on the basis of perception, then you couldn't have been wrong as a matter of chance. Despite the apparent intuitive plausibility of this attitude, which I'll refer to here as "stochastic infallibilism", it fundamentally misunderstands the way that human perceptual systems actually work. Perhaps the most important lesson of signal detection theory (SDT) is that our percepts are inherently subject to random error, and here I'll highlight some key empirical research that underscores this point. In doing so, it becomes clear that we are in fact quite willing to attribute knowledge to S that p even when S's perceptual belief that p could have been randomly false. In short, perceptual processes can randomly fail, and perceptual knowledge is stochastically fallible. The narrow implication here is that any epistemological account that entails stochastic infallibilism, like safety, is simply untenable. More broadly, this myth of stochastic infallibilism provides a valuable illustration of the importance of integrating empirical findings into epistemological thinking. [ABSTRACT FROM AUTHOR]

BackgroundDrugs that induce psychosis may do so by increasing the level of task-irrelevant random neural activity or neural noise. Increased levels of neural noise have been demonstrated in psychotic disorders. We tested the hypothesis that neural noise could also be involved in the psychotomimetic effects of delta-9-tetrahydrocannabinol (Δ(9)-THC), the principal active constituent of cannabis.MethodsNeural noise was indexed by measuring the level of randomness in the electroencephalogram during the prestimulus baseline period of an oddball task using Lempel-Ziv complexity, a nonlinear measure of signal randomness. The acute, dose-related effects of Δ(9)-THC on Lempel-Ziv complexity and signal power were studied in humans (n = 24) who completed 3 test days during which they received intravenous Δ(9)-THC (placebo, .015 and .03 mg/kg) in a double-blind, randomized, crossover, and counterbalanced design.ResultsΔ(9)-THC increased neural noise in a dose-related manner. Furthermore, there was a strong positive relationship between neural noise and the psychosis-like positive and disorganization symptoms induced by Δ(9)-THC, which was independent of total signal power. Instead, there was no relationship between noise and negative-like symptoms. In addition, Δ(9)-THC reduced total signal power during both active drug conditions compared with placebo, but no relationship was detected between signal power and psychosis-like symptoms.ConclusionsAt doses that produced psychosis-like effects, Δ(9)-THC increased neural noise in humans in a dose-dependent manner. Furthermore, increases in neural noise were related with increases in Δ(9)-THC-induced psychosis-like symptoms but not negative-like symptoms. These findings suggest that increases in neural noise may contribute to the psychotomimetic effects of Δ(9)-THC.

Aging is associated with performance decrements across multiple cognitive domains. The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity. Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15-53 years) and scalp EEG data from healthy younger (20-30 years) and older (60-70 years) adults to test the neural noise hypothesis from a 1/f noise perspective. Many natural phenomena, including electrophysiology, are characterized by 1/f noise. The defining characteristic of 1/f is that the power of the signal frequency content decreases rapidly as a function of the frequency (f) itself. The slope of this decay, the noise exponent (χ), is often

Synaptic plasticity is vital for learning and memory in the brain. It consists of long-term potentiation (LTP) and long-term depression (LTD). Spike frequency is one of the major components of synaptic plasticity in the brain, a noisy environment. Recently, we mathematically analyzed the frequency-dependent synaptic plasticity (FDP) in vivo and found that LTP is more likely to occur with an increase in the frequency of background synaptic activity. Meanwhile, previous studies suggest statistical fluctuation in the amplitude of background synaptic activity. Little is understood, however, about its contribution to synaptic plasticity. To address this issue, we performed numerical simulations of a calcium-based synapse model. Then, we found attenuation of the tendency to become LTD due to an increase in the fluctuation of background synaptic activity, leading to an enhancement of synaptic weight. Our result suggests that the fluctuation affects synaptic plasticity in the brain.

Synaptic plasticity is vital for learning and memory in the brain. It consists of long-term potentiation (LTP) and long-term depression (LTD). Spike frequency is one of the major components of synaptic plasticity in the brain, a noisy environment. Recently, we mathematically analyzed the frequency-dependent synaptic plasticity (FDP) in vivo and found that LTP is more likely to occur with an increase in the frequency of background synaptic activity. Meanwhile, previous studies suggest statistical fluctuation in the amplitude of background synaptic activity. Little is understood, however, about its contribution to synaptic plasticity. To address this issue, we performed numerical simulations of a calcium-based synapse model. Then, we found attenuation of the tendency to become LTD due to an increase in the fluctuation of background synaptic activity, leading to an enhancement of synaptic weight. Our result suggests that the fluctuation affects synaptic plasticity in the brain. [ABSTRACT FROM AUTHOR]

Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a “self-consistency” equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model “overcomplete,” which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this “overcomplete” approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

Extracellular recordings of brain voltage signals have many uses, including the identification of spikes and the characterization of brain states via analysis of local field potential (LFP) or EEG recordings. Though the factors underlying the generation of these signals are time varying and complex, their analysis may be facilitated by an understanding of their statistical properties. To this end, we analyzed the voltage distributions of high-pass extracellular recordings from a variety of structures, including cortex, thalamus, and hippocampus, in monkeys, cats, and rodents. We additionally investigated LFP signals in these recordings as well as human EEG signals obtained during different sleep stages. In all cases, the distributions were accurately described by a Gaussian within ±1.5 standard deviations from zero. Outside these limits, voltages tended to be distributed exponentially, that is, they fell off linearly on log-linear frequency plots, with variable heights and slopes. A possible explanation for this is that sporadically and independently occurring events with individual Gaussian size distributions can sum to produce approximately exponential distributions. For the high-pass recordings, a second explanation results from a model of the noisy behavior of ion channels that produce action potentials via Hodgkin-Huxley kinetics. The distributions produced by this model, relative to the averaged potential, were also Gaussian with approximately exponential flanks. The model also predicted time-varying noise distributions during action potentials, which were observed in the extracellular spike signals. These findings suggest a principled method for detecting spikes in high-pass recordings and transient events in LFP and EEG signals. NEW & NOTEWORTHY We show that the voltage distributions in brain recordings, including high-pass extracellular recordings, the LFP, and human EEG, are accurately described by a Gaussian within ±1.5 standard deviations from zero, with heavy, exponential tails outside these limits. This offers a principled way of setting event detection thresholds in high-pass recordings. It also offers a means for identifying event-like, transient signals in LFP and EEG recordings which may correlate with other neural phenomena. [ABSTRACT FROM AUTHOR]

Contradictory results have been obtained in the studies that compare contour integration abilities in Autism Spectrum Disorders (ASDs) and typically developing individuals. The present study aimed to explore the limiting factors of contour integration ability in ASD and verify the role of the external visual noise by a combination of psychophysical and eye-tracking approaches. To this aim, 24 children and adolescents with ASD and 32 age-matched participants with typical development had to detect the presence of contour embedded among similar Gabor elements in a Yes/No procedure. The results obtained showed that the responses in the group with ASD were not only less accurate but also were significantly slower compared to the control group at all noise levels. The detection performance depended on the group differences in addition to the effect of the intellectual functioning of the participants from both groups. The comparison of the agreement and accuracy of the responses in the double-pass experiment showed that the results of the participants with ASD are more affected by the increase of the external noise. It turned out that the internal noise depends on the level of the added external noise: the difference between the two groups was non-significant at the low external noise and significant at the high external noise. In accordance with the psychophysical results, the eye-tracking data indicated a larger gaze allocation area in the group with autism. These findings may imply higher positional uncertainty in ASD due to the inability to maintain the information of the contour location from previous presentations and interference from noise elements in the contour vicinity. Psychophysical and eye-tracking data suggest lower efficiency in using stimulus information in the ASD group that could be caused by fixation instability and noisy and unstable perceptual template that affects noise filtering.

Contradictory results have been obtained in the studies that compare contour integration abilities in Autism Spectrum Disorders (ASDs) and typically developing individuals. The present study aimed to explore the limiting factors of contour integration ability in ASD and verify the role of the external visual noise by a combination of psychophysical and eye-tracking approaches. To this aim, 24 children and adolescents with ASD and 32 age-matched participants with typical development had to detect the presence of contour embedded among similar Gabor elements in a Yes/No procedure. The results obtained showed that the responses in the group with ASD were not only less accurate but also were significantly slower compared to the control group at all noise levels. The detection performance depended on the group differences in addition to the effect of the intellectual functioning of the participants from both groups. The comparison of the agreement and accuracy of the responses in the double-pass experiment showed that the results of the participants with ASD are more affected by the increase of the external noise. It turned out that the internal noise depends on the level of the added external noise: the difference between the two groups was non-significant at the low external noise and significant at the high external noise. In accordance with the psychophysical results, the eye-tracking data indicated a larger gaze allocation area in the group with autism. These findings may imply higher positional uncertainty in ASD due to the inability to maintain the information of the contour location from previous presentations and interference from noise elements in the contour vicinity. Psychophysical and eye-tracking data suggest lower efficiency in using stimulus information in the ASD group that could be caused by fixation instability and noisy and unstable perceptual template that affects noise filtering. [ABSTRACT FROM AUTHOR]

Finding a mathematical model that incorporates various stochastic aspects of neural dynamics has proven to be a continuous challenge. Among the different approaches, the noisy leaky integrate-and-fire and the escape rate models are probably the most popular. These two models are generally thought to express different noise action over the neural cell. In this paper we investigate the link between the two formalisms in the case of a neuron subject to a time dependent input. To this aim, we introduce a new general stochastic framework. As we shall prove, our general framework entails the two already existing ones. Our results have theoretical implications since they offer a general view upon the two stochastic processes mostly used in neuroscience, upon the way they can be linked, and explain their observed statistical similarity. [ABSTRACT FROM AUTHOR]

Neural information processing is subject to noise and this leads to variability in neural firing and behavior. Schizophrenia has been associated with both more variable motor control and impaired cortical inhibition, which is crucial for excitatory/inhibitory balance in neural commands. In this study, we hypothesized that impaired intracortical inhibition in motor cortex would contribute to task-related motor noise in schizophrenia. We measured variability of force and of electromyographic (EMG) activity in upper limb and hand muscles during a visuomotor grip force-tracking paradigm in patients with schizophrenia (N = 25), in unaffected siblings (N = 17) and in healthy control participants (N = 25). Task-dependent primary motor cortex (M1) excitability and inhibition were assessed using transcranial magnetic stimulation (TMS). During force maintenance patients with schizophrenia showed increased variability in force and EMG, despite similar mean force and EMG magnitudes. Compared to healthy controls, patients with schizophrenia also showed increased M1 excitability and reduced cortical inhibition during grip-force tracking. EMG variability and force variability correlated negatively to cortical inhibition in patients with schizophrenia. EMG variability also correlated positively to negative symptoms. Siblings had similar variability and cortical inhibition compared to controls. Increased EMG and force variability indicate enhanced motor noise in schizophrenia, which relates to reduced motor cortex inhibition. The findings suggest that excessive motor noise in schizophrenia may arise from an imbalance of M1 excitation/inhibition of GABAergic origin. Thus, higher motor noise may provide a useful marker of impaired cortical inhibition in schizophrenia. • Increased variability in force and EMG during grip maintenance in schizophrenia. • Altered task-related cortical excitability and inhibition in primary motor cortex. • Variability of force and EMG were both correlated with altered cortical inhibition. • Cortical excitation/inhibition imbalance likely contributing to motor noise. [ABSTRACT FROM AUTHOR]

Tinnitus is a sound heard by 15% of the general population in the absence of any external sound. Because external sounds can sometimes mask tinnitus, tinnitus is assumed to affect the perception of external sounds, leading to hypotheses such as "tinnitus filling in the temporal gap" in animal models and "tinnitus inducing hearing difficulty" in human subjects. Here we compared performance in temporal, spectral, intensive, masking and speech-in-noise perception tasks between 45 human listeners with chronic tinnitus (18 females and 27 males with a range of ages and degrees of hearing loss) and 27 young, normal- hearing listeners without tinnitus (11 females and 16 males). After controlling for age, hearing loss, and stimulus variables, we discovered that, contradictory to the widely held assumption, tinnitus does not interfere with the perception of external sounds in 32 of the 36 measures. We interpret the present result to reflect a bottom-up pathway for the external sound and a separate top-down pathway for tinnitus. We propose that these two perceptual pathways can be independently modulated by attention, which leads to the asymmetrical interaction between external and internal sounds, and several other puzzling tinnitus phenomena such as discrepancy in loudness between tinnitus rating and matching. The present results suggest not only a need for new theories involving attention and central noise in animal tinnitus models but also a shift in focus from treating tinnitus to managing its comorbid conditions when addressing complaints about hearing difficulty in individuals with tinnitus. [ABSTRACT FROM AUTHOR]

Internal stochastic resonance (internal SR) is a phenomenon of non-linear systems in which the addition of a non-zero level of noise produces an enhancement in the coherence between two or more signals. In a previous study, we found that the simultaneous administration of multisensory visual and auditory noise augments global coherence in electroencephalographic (EEG) signals via this phenomenon. Here, we examined whether such global coherence can also be augmented with at least one noisy acoustic source. We performed experiments on healthy subjects and applied the following binaural and monaural noise-stimulation protocols. First, we administered to the left ear Gaussian noise of fixed intensity, while we delivered to the right ear a second Gaussian noise of variable intensity levels (binaural protocol). Second, we applied the Gaussian noise of the same variable intensity levels but only to one ear (monaural protocol). We performed a permutation test analysis, finding that during both noise protocols there was a significant enhancement in the global coherence in EEG signals via the occurrence of internal SR within central pathways of the auditory system. [ABSTRACT FROM AUTHOR]

Background: Playing sports has many benefits, including boosting physical, cardiovascular, and mental fitness. We tested whether athletic benefits extend to sensory processing-specifically auditory processing-as measured by the frequency-following response (FFR), a scalp-recorded electrophysiological potential that captures neural activity predominately from the auditory midbrain to complex sounds.Hypothesis: Given that FFR amplitude is sensitive to experience, with enrichment enhancing FFRs and injury reducing them, we hypothesized that playing sports is a form of enrichment that results in greater FFR amplitude.Study Design: Cross-sectional study.Level Of Evidence: Level 3.Methods: We measured FFRs to the speech syllable "da" in 495 student-athletes across 19 Division I teams and 493 age- and sex-matched controls and compared them on 3 measures of FFR amplitude: amplitude of the response, amplitude of the background noise, and the ratio of these 2 measures.Results: Athletes have larger responses to sound than nonathletes, driven by a reduction in their level of background neural noise.Conclusion: These findings suggest that playing sports increases the gain of an auditory signal by turning down the background noise. This mode of enhancement may be tied to the overall fitness level of athletes and/or the heightened need of an athlete to engage with and respond to auditory stimuli during competition.Clinical Relevance: These results motivate athletics overall and engagement in athletic interventions for populations that struggle with sensory processing, such as individuals with language disorders. Also, because head injuries can disrupt these same auditory processes, it is important to consider how auditory processing enhancements may offset injury. [ABSTRACT FROM AUTHOR]

The general goal of the interdisciplinary work refers to the research of complex experimental interactions and theoretical works on the subject of neural mechanisms in the perception of decision making; economic and perceptual decision making; high and low volatility bias of the investors perception, and the perception bias during the duration of the stimuli, according to the theory of subsequent effect. The work shows the complex interweaving of scientific achievements in the process of decision making. The given scientific and applicative research leads us towards understanding the levels of complexity of financial decision making with the principles of universality; spatial and temporal fluctuations of input in perceptual decision making (perception can be under the influence of attention and can surface subconsciously without conscious consciousness), possible extending of current results and models from two alternative choices and are they different in respect to spatial and temporal fluctuations ( our capability of deciding can result from random fluctuations in the background of electric noise in the brain) effects on the results of decision making. The focus of this research paper is the analysis of testing the perception of investors which shows us the subsequent effect of volatility, which further indicates the twisted perception after prolonged exposure to extreme levels of volatility. This established framework can give us key insight in the domain of deductive reasoning. Bias in deductions is questioned using the VIX index.

The Simple View of Reading model suggests that intact language processing and word decoding lead to proficient reading comprehension, with recent studies pointing at executive functions as an important component contributing to reading proficiency. Here, we aimed to determine the underlying mechanism(s) for these changes. Participants include 120 8- to 12-year-old children (n = 55 with dyslexia, n = 65 typical readers) trained on an executive functions-based reading program, including pre/postfunctional MRI and behavioral data collection. Across groups, improved word reading was related to stronger functional connections within executive functions and sensory networks. In children with dyslexia, faster and more accurate word reading was related to stronger functional connections within and between sensory networks. These results suggest greater synchronization of brain systems after the intervention, consistent with the "neural noise" hypothesis in children with dyslexia and support the consideration of including executive functions as part of the Simple View of Reading model., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Individuals with migraine aura show differences in visual perception compared to control groups. Measures of contrast sensitivity have suggested that people with migraine aura are less able to exclude external visual noise, and that this relates to higher variability in neural processing. The current study compared contrast sensitivity in migraine with aura and control groups for narrow-band grating stimuli at 2 and 8 cycles/degree, masked by Gaussian white noise. We predicted that contrast sensitivity would be lower in the migraine with aura group at high noise levels. Contrast sensitivity was higher for the low spatial frequency stimuli, and decreased with the strength of the masking noise. We did not, however, find any evidence of reduced contrast sensitivity associated with migraine with aura. We propose alternative methods as a more targeted assessment of the role of neural noise and excitability as contributing factors to migraine aura.

For some people, simple sensory stimuli (e.g., noises, patterns) may reliably evoke intense and aversive reactions. This is common in certain clinical groups (e.g., autism) and varies greatly in the neurotypical population. This paper critically evaluates the concept of individual differences in sensory sensitivity, explores its possible underlying neurobiological basis, and presents a roadmap for future research in this area. A distinction is made between subjective sensory sensitivity (self-reported symptoms); neural sensory sensitivity (the degree of neural activity induced by sensory stimuli); and behavioral sensory sensitivity (detection and discrimination of sensory stimuli). Whereas increased subjective and neural sensory sensitivity are assumed to increase together, the status of behavioral sensory sensitivity depends on the extent to which the increased neural activity is linked to signal or noise. A signal detection framework is presented that offers a unifying framework for exploring sensory sensitivity across different conditions. The framework is discussed, in more concrete terms, by linking it to four existing theoretical accounts of atypical sensory sensitivity (not necessarily mutually exclusive): increased excitation-to-inhibition ratio; predictive coding; increased neural noise; and atypical brain connectivity. [ABSTRACT FROM AUTHOR]

Several lines of evidence suggest that there is a close interrelation between the degree of noise in neural circuits and the activity of the norepinephrine (NE) system, yet the precise nexus between these aspects is far from being understood during human information processing and cognitive control in particular. We examine this nexus during response inhibition in n = 47 healthy participants. Using high-density EEG recordings, we estimate neural noise by calculating "1/f noise" of those data and integrate these EEG parameters with pupil diameter data as an established indirect index of NE system activity. We show that neural noise is reduced when cognitive control processes to inhibit a prepotent/automated response are exerted. These neural noise variations were confined to the theta frequency band, which has also been shown to play a central role during response inhibition and cognitive control. There were strong positive correlations between the 1/f neural noise parameter and the pupil diameter data within the first 250 ms after the Nogo stimulus presentation at centro-parietal electrode sites. No such correlations were evident during automated responding on Go trials. Source localization analyses using standardized low-resolution brain electromagnetic tomography show that inferior parietal areas are activated in this time period in Nogo trials. The data suggest an interrelation of NE system activity and neural noise within early stages of information processing associated with inferior parietal areas when cognitive control processes are required. The data provide the first direct evidence for the nexus between NE system activity and the modulation of neural noise during inhibitory control in humans. [ABSTRACT FROM AUTHOR]

In adulthood, neurological structure and function are often affected by aging, with negative implications for daily life as well as laboratory-based tasks. Some of these changes include decreased efficiency modulating cortical activity and lower signal-to-noise ratios in neural processing (as inferred from surface electroencephalography). To better understand mechanisms influencing age-related changes in cortical activity, we explored the effects of aging on narrow-band alpha power (7.5–12.5 Hz) and broadband/aperiodic components that span a wider range (1.5–30.5 Hz) over the occipital region during eyes-open and eyes-closed wakeful rest in 19 healthy young adults (18–35 years) and 21 community-dwelling older adults (59+ years). Older adults exhibited a smaller change in alpha power across conditions compared to younger adults. Older adults also showed flatter aperiodic slopes in both conditions. These changes in narrow-band alpha are consistent with previous work and suggest that older adults may have a reduced ability to modulate state-specific activity. Differences in the aperiodic slope suggest age-related changes in the signal-noise-ratio in cortical oscillations. However, the relationship between narrow-band alpha modulation and the aperiodic slope was unclear, warranting further investigation into how these variables relate to each other in the aging process. In summary, aging is associated with a broadband flattening of the EEG power spectrum and reduced state-specific modulation of narrow-band alpha power, but these changes appear to be (at least partially) independent of each other. The present findings suggest that separate mechanisms may underlie age-related differences in aperiodic power and narrow-band oscillations. • Narrowband occipital alpha power (7.5–12.5 Hz) increases from eyes-open to eyes-closed rest. • Older adults show less of a change in occipital alpha power compared to younger adults. • Older adults show a flatter power spectrum (1.5–30.5 Hz) over occipital cortex compared to younger adults. • Age-related differences in alpha power remain even after accounting for age-related differences in the broadband slope. [ABSTRACT FROM AUTHOR]

Dyslexia is a neurodevelopmental disorder with an atypical activation of posterior left-hemisphere brain reading networks (i.e., temporo-occipital and temporo-parietal regions) and multiple neuropsychological deficits. Transcranial direct current stimulation (tDCS) is a tool for manipulating neural activity and, in turn, neurocognitive processes. While studies have demonstrated the significant effects of tDCS on reading, neurocognitive changes beyond reading modulation have been poorly investigated. The present study aimed at examining whether tDCS on temporo-parietal regions affected not only reading, but also phonological skills, visuo-spatial working memory, visuo-spatial attention, and motion perception in a polarity-dependent way. In a within-subjects design, ten children and adolescents with dyslexia performed reading and neuropsychological tasks after 20 min of exposure to Left Anodal/Right Cathodal (LA/RC) and Right Anodal/Left Cathodal (RA/LC) tDCS. LA/RC tDCS compared to RA/LC tDCS improved text accuracy, word recognition speed, motion perception, and modified attentional focusing in our group of children and adolescents with dyslexia. Changes in text reading accuracy and word recognition speed—after LA/RC tDCS compared to RA/LC—were related to changes in motion perception and in visuo-spatial working memory, respectively. Our findings demonstrated that reading and domain-general neurocognitive functions in a group of children and adolescents with dyslexia change following tDCS and that they are polarity-dependent.

We study how sensory neurons detect and transmit a weak external stimulus. We use the FitzHugh–Nagumo model to simulate the neuronal activity. We consider a sub-threshold stimulus, i.e., the stimulus is below the threshold needed for triggering action potentials (spikes). However, in the presence of noise the neuron that perceives the stimulus fires a sequence of action potentials (a spike train) that carries the stimulus’ information. To yield light on how the stimulus’ information can be encoded and transmitted, we consider the simplest case of two coupled neurons, such that one neuron (referred to as neuron 1) perceives a subthreshold periodic signal but the second neuron (neuron 2) does not perceive the signal. We show that, for appropriate coupling and noise strengths, both neurons fire spike trains that have symbolic patterns (defined by the temporal structure of the inter-spike intervals), whose frequencies of occurrence depend on the signal’s amplitude and period, and are similar for both neurons. In this way, the signal information encoded in the spike train of neuron 1 propagates to the spike train of neuron 2. Our results suggest that sensory neurons can exploit the presence of neural noise to fire spike trains where the information of a subthreshold stimulus is encoded in over expressed and/or in less expressed symbolic patterns.