Variability Of Neural Responses Research Articles

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation.

Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data. First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using a large-scale dataset of scalp EEG during motor imagery-based BCI use and independent dataset for generalizability confirmation (N = 147). Next, we characterized the relationship between IAF and responsive frequency band of sensorimotor rhythm, which exhibits prominent event-related desynchronization (SMR-ERD) while attempting unilateral and movement. The proposed sequential Bayesian estimation algorithm (Rapid-IAF) determined IAF from less than 26-second resting EEG data among 95% of participants, indicating a clear advance over the conventional methods, which uses 2-15 minutes of data in previous literatures. We confirmed that the determined IAF corresponded to the frequency of SMR, which exhibits the most prominent event-related desynchronization during BCI use (individual SMR-ERD frequency, ISF). Moreover, intraclass correlation revealed that the estimated IAF was more stable than ISF across sessions, suggesting its reliability and utility for robust BCI use without intermittent recalibration. Conclusion In summary, our method rapidly and reliably determined IAF compared to the conventional method using the spectral power change based on task-related response. The method can be utilized to quick BCI initialization. The demonstration of rapid, task-free parametrization of individual variability of neural responses would be of importance for future BCI systems including neural communication via a cursor, an avatar or robots, and closed-loop neurofeedback training.

Higher Intersubject Variability in Neural Response to Narrative Social Stimuli Among Youth With Higher Social Anxiety

Social anxiety is associated with alterations in socioemotional processing, but the pathophysiology remains poorly understood. Movies present an opportunity to examine more naturalistic socioemotional processing by providing narrative and sensory context to emotion cues. This study aimed to characterize associations between neural response to contextualized social cues and social anxiety symptoms in children. Data from the Healthy Brain Network (final N= 740; age range 5-15 years) were split into discovery and replication samples to maximize generalizability of findings. Associations of parent- and self-reported social anxiety (Screen for Child Anxiety-related Emotional Disorders) with mean differences and person-to-person variability in functional magnetic resonance imaging-measured activation to 2 emotionally dynamic movies were characterized. Though no evidence was found to indicate social anxiety symptoms were associated with mean differences in neural activity to emotional content (fit Spearman rs< 0.09), children with high social anxiety symptoms had higher intersubject activation variability in the posterior cingulate, supramarginal gyrus, and inferior frontal gyrus (Bonferroni familywise error-corrected ps< .05)-regions associated with attention, alertness, and emotion cue processing. Identified regions varied by age group and informant. Across ages, these effects were enhanced for scenes containing greater sensory intensity (brighter, louder, more motion, more vibrance). These results provide evidence that children with high social anxiety symptoms show high person-to-person variability in the neural processing of sensory aspects of emotional content. These data indicate that children with high social anxiety may require personalized interventions for sensory and emotional difficulties, as the underlying neurology differs from child to child. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list.

Modeling the effects of transcranial magnetic stimulation on spatial attention

Objectives. Transcranial magnetic stimulation (TMS) has been widely used to modulate brain activity in healthy and diseased brains, but the underlying mechanisms are not fully understood. Previous research leveraged biophysical modeling of the induced electric field (E-field) to map causal structure–function relationships in the primary motor cortex. This study aims at transferring this localization approach to spatial attention, which helps to understand the TMS effects on cognitive functions, and may ultimately optimize stimulation schemes. Approach. Thirty right-handed healthy participants underwent a functional magnetic imaging (fMRI) experiment, and seventeen of them participated in a TMS experiment. The individual fMRI activation peak within the right inferior parietal lobule (rIPL) during a Posner-like attention task defined the center target for TMS. Thereafter, participants underwent 500 Posner task trials. During each trial, a 5-pulse burst of 10 Hz repetitive TMS (rTMS) was given over the rIPL to modulate attentional processing. The TMS-induced E-fields for every cortical target were correlated with the behavioral modulation to identify relevant cortical regions for attentional orientation and reorientation. Main results. We did not observe a robust correlation between E-field strength and behavioral outcomes, highlighting the challenges of transferring the localization method to cognitive functions with high neural response variability and complex network interactions. Nevertheless, TMS selectively inhibited attentional reorienting in five out of seventeen subjects, resulting in task-specific behavioral impairments. The BOLD-measured neuronal activity and TMS-evoked neuronal effects showed different patterns, which emphasizes the principal distinction between the neural activity being correlated with (or maybe even caused by) particular paradigms, and the activity of neural populations exerting a causal influence on the behavioral outcome. Significance. This study is the first to explore the mechanisms of TMS-induced attentional modulation through electrical field modeling. Our findings highlight the complexity of cognitive functions and provide a basis for optimizing attentional stimulation protocols.

Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems

Abstract Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either due to lack of connectivity or for privacy concerns. However, these circuits are typically noisy and imprecise, because they are affected by device-to-device variability, and operate with extremely small currents. So achieving reliable computation and high accuracy following this approach is still an open challenge that has hampered progress on the one hand and limited widespread adoption of this technology on the other. By construction, these hardware processing systems have many constraints that are biologically plausible, such as heterogeneity and non-negativity of parameters. More and more evidence is showing that applying such constraints to artificial neural networks, including those used in artificial intelligence, promotes robustness in learning and improves their reliability. Here we delve even more into neuroscience and present network-level brain-inspired strategies that further improve reliability and robustness in these neuromorphic systems: we quantify, with chip measurements, to what extent population averaging is effective in reducing variability in neural responses, we demonstrate experimentally how the neural coding strategies of cortical models allow silicon neurons to produce reliable signal representations, and show how to robustly implement essential computational primitives, such as selective amplification, signal restoration, working memory, and relational networks, exploiting such strategies. We argue that these strategies can be instrumental for guiding the design of robust and reliable ultra-low power electronic neural processing systems implemented using noisy and imprecise computing substrates such as subthreshold neuromorphic circuits and emerging memory technologies.

EEG repetition and change detection responses in infancy predict adaptive functioning in preschool age: a longitudinal study

Neurodevelopmental disorders (NDDs) are mostly diagnosed around the age of 4–5 years, which is too late considering that the brain is most susceptive to interventions during the first two years of life. Currently, diagnosis of NDDs is based on observed behaviors and symptoms, but identification of objective biomarkers would allow for earlier screening. In this longitudinal study, we investigated the relationship between repetition and change detection responses measured using an EEG oddball task during the first year of life and at two years of age, and cognitive abilities and adaptive functioning during preschool years (4 years old). Identification of early biomarkers is challenging given that there is a lot of variability in developmental courses among young infants. Therefore, the second aim of this study is to assess whether brain growth is a factor of interindividual variability that influences repetition and change detection responses. To obtain variability in brain growth beyond the normative range, infants with macrocephaly were included in our sample. Thus, 43 normocephalic children and 20 macrocephalic children were tested. Cognitive abilities at preschool age were assessed with the WPPSI-IV and adaptive functioning was measured with the ABAS-II. Time–frequency analyses were conducted on the EEG data. Results indicated that repetition and change detection responses in the first year of life predict adaptive functioning at 4 years of age, independently of head circumference. Moreover, our findings suggested that brain growth explains variability in neural responses mostly in the first years of life, so that macrocephalic children did not display repetition suppression responses, while normocephalic children did. This longitudinal study demonstrates that the first year of life is an important period for the early screening of children at risk of developing NDDs.

Reduced pattern similarity in brain activation during orthographic processing in children with developmental dyslexia

Intra-individual variability of neural response has been found to be negatively associated with cognitive proficiency and automaticity. However, whether developmental dyslexia (DD) is marked by greater intra-individual neural variability remains unclear. Using a multivariate approach and dual-control group design, the current study aims to examine whether the pattern similarity of brain activation during a visual spelling task is abnormal in children with DD compared to age control and reading control children. We found that there was reduced intra-subject pattern similarity at the left occipito-temporal regions in children with DD than both control groups, suggesting a neural signature of DD. Furthermore, we found that pattern similarity was positively associated with stability of reaction time and reading fluency in both children with DD and typical control children, suggesting that neural stability supports behavioral stability and automaticity during reading.

Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions.

Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contributions of within-trial spike irregularity and trial-to-trial rate variability to FF fluctuations have remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time but could be enhanced due to factors such as bursting abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying spiking irregularity arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach reveals previously unnoticed changes in neural response variability and constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain–Computer Interfaces (BCIs) technology. However, the substantial inter/intra variability of recorded data significantly influences individual skills on the achieved performance. This study explores the ability to distinguish between MI tasks and the interpretability of the brain’s ability to produce elicited mental responses with improved accuracy. We develop a Deep and Wide Convolutional Neuronal Network fed by a set of topoplots extracted from the multichannel EEG data. Further, we perform a visualization technique based on gradient-based class activation maps (namely, GradCam++) at different intervals along the MI paradigm timeline to account for intra-subject variability in neural responses over time. We also cluster the dynamic spatial representation of the extracted maps across the subject set to come to a deeper understanding of MI-BCI coordination skills. According to the results obtained from the evaluated GigaScience Database of motor-evoked potentials, the developed approach enhances the physiological explanation of motor imagery in aspects such as neural synchronization between rhythms, brain lateralization, and the ability to predict the MI onset responses and their evolution during training sessions.

Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions.

Most models of neural responses are constructed to reproduce the average response to inputs but lack the flexibility to capture observed variability in responses. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system, both by limiting information that can be conveyed and by determining processing strategies that are favorable for minimizing its negative effects. Here, we present a new modeling framework that incorporates multiple sources of noise to better capture observed features of neural response variability across stimulus conditions. We apply this model to retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. Further, the model reveals light level-dependent changes that could not be seen with previous models, showing both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions.

Neurophysiological markers of ADHD symptoms in typically-developing children

Children with attention-deficit/hyperactivity disorder (ADHD) are characterized by symptoms of inattention, impulsivity, and hyperactivity. Neurophysiological correlates of ADHD include changes in the P3 component of event-related brain potentials (ERPs). Motivated by recent advances towards a more dimensional understanding of ADHD, we investigate whether ADHD-related ERP markers relate to continuous variations in attention and executive functioning also in typically-developing children. ERPs were measured while 31 school children (9–11 years) completed an adapted version of the Continuous Performance Task that additionally to inhibitory processes also isolates effects of physical stimulus salience. Children with higher levels of parent-reported ADHD symptoms did not differ in task performance, but exhibited smaller P3 amplitudes related to stimulus salience. Furthermore, ADHD symptoms were associated with the variability of neural responses over time: Children with higher levels of ADHD symptoms demonstrated lower variability in inhibition- and salience-related P3 amplitudes. No effects were observed for ERP latencies and the salience-related N2. By demonstrating that ADHD-associated neurophysiological mechanisms of inhibition and salience processing covary with attention and executive functioning in a children community sample, our study provides neurophysiological support for dimensional models of ADHD. Also, temporal variability in event-related potentials is highlighted as additional indicator of ADHD requiring further investigation.

Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses.

Brain responses vary considerably from moment to moment, even to identical sensory stimuli. This has been attributed to changes in instantaneous neuronal states determining the system's excitability. Yet the spatiotemporal organization of these dynamics remains poorly understood. Here we test whether variability in stimulus-evoked activity can be interpreted within the framework of criticality, which postulates dynamics of neural systems to be tuned toward the phase transition between stability and instability as is reflected in scale-free fluctuations in spontaneous neural activity. Using a novel noninvasive approach in 33 male human participants, we tracked instantaneous cortical excitability by inferring the magnitude of excitatory postsynaptic currents from the N20 component of the somatosensory evoked potential. Fluctuations of cortical excitability demonstrated long-range temporal dependencies decaying according to a power law across trials, a hallmark of systems at critical states. As these dynamics covaried with changes in prestimulus oscillatory activity in the alpha band (8-13 Hz), we establish a mechanistic link between ongoing and evoked activity through cortical excitability and argue that the co-emergence of common temporal power laws may indeed originate from neural networks poised close to a critical state. In contrast, no signatures of criticality were found in subcortical or peripheral nerve activity. Thus, criticality may represent a parsimonious organizing principle of variability in stimulus-related brain processes on a cortical level, possibly reflecting a delicate equilibrium between robustness and flexibility of neural responses to external stimuli.SIGNIFICANCE STATEMENT Variability of neural responses in primary sensory areas is puzzling, as it is detrimental to the exact mapping between stimulus features and neural activity. However, such variability can be beneficial for information processing in neural networks if it is of a specific nature, namely, if dynamics are poised at a so-called critical state characterized by a scale-free spatiotemporal structure. Here, we demonstrate the existence of a link between signatures of criticality in ongoing and evoked activity through cortical excitability, which fills the long-standing gap between two major directions of research on neural variability: the impact of instantaneous brain states on stimulus processing on the one hand and the scale-free organization of spatiotemporal network dynamics of spontaneous activity on the other.

Toward an Individualized Neural Assessment of Receptive Language in Children.

Purpose We aimed to develop a noninvasive neural test of language comprehension to use with nonspeaking children for whom standard behavioral testing is unreliable (e.g., minimally verbal autism). Our aims were threefold. First, we sought to establish the sensitivity of two auditory paradigms to elicit neural responses in individual neurotypical children. Second, we aimed to validate the use of a portable and accessible electroencephalography (EEG) system, by comparing its recordings to those of a research-grade system. Third, in light of substantial interindividual variability in individuals' neural responses, we assessed whether multivariate decoding methods could improve sensitivity. Method We tested the sensitivity of two child-friendly covert N400 paradigms. Thirty-one typically developing children listened to identical spoken words that were either strongly predicted by the preceding context or violated lexical-semantic expectations. Context was given by a cue word (Experiment 1) or sentence frame (Experiment 2), and participants either made an overall judgment on word relatedness or counted lexical-semantic violations. We measured EEG concurrently from a research-grade system, Neuroscan's SynAmps2, and an adapted gaming system, Emotiv's EPOC+. Results We found substantial interindividual variability in the timing and topology of N400-like effects. For both paradigms and EEG systems, traditional N400 effects at the expected sensors and time points were statistically significant in around 50% of individuals. Using multivariate analyses, detection rate increased to 88% of individuals for the research-grade system in the sentences paradigm, illustrating the robustness of this method in the face of interindividual variations in topography. Conclusions There was large interindividual variability in neural responses, suggesting interindividual variation in either the cognitive response to lexical-semantic violations and/or the neural substrate of that response. Around half of our neurotypical participants showed the expected N400 effect at the expected location and time points. A low-cost, accessible EEG system provided comparable data for univariate analysis but was not well suited to multivariate decoding. However, multivariate analyses with a research-grade EEG system increased our detection rate to 88% of individuals. This approach provides a strong foundation to establish a neural index of language comprehension in children with limited communication. Supplemental Material https://doi.org/10.23641/asha.12606311.

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks.

Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.

Surprise response as a probe for compressed memory states

The limited capacity of recent memory inevitably leads to partial memory of past stimuli. There is also evidence that behavioral and neural responses to novel or rare stimuli are dependent on one’s memory of past stimuli. Thus, these responses may serve as a probe of different individuals’ remembering and forgetting characteristics. Here, we utilize two lossy compression models of stimulus sequences that inherently involve forgetting, which in addition to being a necessity under many conditions, also has theoretical and behavioral advantages. One model is based on a simple stimulus counter and the other on the Information Bottleneck (IB) framework which suggests a more general, theoretically justifiable principle for biological and cognitive phenomena. These models are applied to analyze a novelty-detection event-related potential commonly known as the P300. The trial-by-trial variations of the P300 response, recorded in an auditory oddball paradigm, were subjected to each model to extract two stimulus-compression parameters for each subject: memory length and representation accuracy. These parameters were then utilized to estimate the subjects’ recent memory capacity limit under the task conditions. The results, along with recently published findings on single neurons and the IB model, underscore how a lossy compression framework can be utilized to account for trial-by-trial variability of neural responses at different spatial scales and in different individuals, while at the same time providing estimates of individual memory characteristics at different levels of representation using a theoretically-based parsimonious model.

Greater Individual Variability in Functional Brain Activity during Working Memory Performance in young people with Autism and Executive Function Impairment.

Individuals with autism spectrum disorder (ASD) often present with executive functioning (EF) deficits, including spatial working memory (SWM) impairment, which impedes real-world functioning. The present study examined task-related brain activity, connectivity and individual variability in fMRI-measured neural response during an SWM task in older youth and young adults with autism and clinically significant EF impairment. Neuroimaging was analyzed in 29 individuals with ASD without intellectual disability who had clinically significant EF impairment on the Behavior Rating Inventory of Executive Function, and 20 typically developing controls (participant age range=16-34). An SWM N-Back task was performed during fMRI. SWM activity (2-Back vs. 0-Back) and task-related dorsolateral prefrontal cortex (DLPFC) connectivity was examined within and between groups. Variability of neural response during SWM was also examined. During SWM performance both groups activated the expected networks, and no group differences in network activation or task-related DLPFC-connectivity were found. However, greater individual variability in the pattern of SWM activity was found in the ASD versus the typically developing control group. While there were no group differences in SWM task-evoked activity or connectivity, fronto-parietal network engagement was found to be more variable/idiosyncratic in ASD. Our results suggest that the fronto-parietal network may be shifted or sub-optimally engaged during SWM performance in participants with ASD with clinically significant EF impairment, with implications for developing targeted interventions for this subgroup.

Reduced visual evoked potential amplitude in autism spectrum disorder, a variability effect?

Atypical sensory behaviours represent a core symptom of autism spectrum disorder (ASD). Investigating early visual processing is crucial to deepen our understanding of higher-level processes. Visual evoked potentials (VEPs) to pattern-reversal checkerboards were recorded in ASD children and age-matched controls. Peak analysis of the P100 component and two types of single-trial analyses were carried out. P100 amplitude was reduced in the ASD group, consistent with previous reports. The analysis of the proportion of trials with a positive activity in the latency range of the P100, measuring inter-trial (in)consistency, allowed identifying two subgroups of ASD participants: the first group, as control children, showed a high inter-trial consistency, whereas the other group showed an inter-trial inconsistency. Analysis of median absolute deviation of single-trial P100 (st-P100) latencies revealed an increased latency variability in the ASD group. Both single-trial analyses revealed increased variability in a subset of children with ASD. To control for this variability, VEPs were reconstructed by including only positive trials or trials with homogeneous st-P100 latencies. These control analyses abolished group differences, confirming that the reduced P100 amplitude results from increased inter-trial variability in ASD. This increased variability in ASD supports the neural noise theory. The existence of subgroups in ASD suggests that the neural response variability is not a genuine characteristic of the entire autistic spectrum, but rather characterized subgroups of children. Exploring the relationship between sensory responsiveness and inter-trial variability could provide more precise bioclinical profiles in children with ASD, and complete the functional diagnostic crucial for the development of individualized therapeutical projects.

Modeling stimulus-dependent variability improves decoding of population neural responses

Objective. Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. Approach. In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models—the Conway–Maxwell–Poisson (CMP) model and negative binomial (NB) model—to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor > 1) or, for the CMP model, less than the mean (Fano factor < 1). Main results. We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4%–12% compared to the Poisson model. Significance. Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.

Atypical neural variability in carriers of 16p11.2 copy number variants.

Copy number variations (CNVs) at the 16p11.2 chromosomal region are associated with myriad clinical features including intellectual disability and autism spectrum disorder. The aim of this study is to determine whether 16p11.2 deletion (DEL) and duplication (DUP) carriers demonstrate a distinct and reciprocal pattern of electroencephalography (EEG) activity as represented by neural variability measures. EEG data were previously collected as part of the Simons Variation in Individuals Project. Variability measures, as estimated by single-trial ERP and spectral power analyses in the alpha and beta frequency bands, in addition to signal-to-noise ratios (SNRs), were analyzed in DEL (n = 20), DUP (n = 8), and typical (n = 11) groups. We also analyzed mean visual evoked potentials and spectral power (alpha and beta power) to facilitate comparisons with other studies of associated disorders and CNVs. From measures of single-trial variability, we found higher intraparticipant variability in P1 amplitude and timecourse amplitude in DEL compared to controls. Compared to DUP, DEL showed higher variability in absolute alpha and absolute beta power but lower variability in P1 latency. SNRs did not differ between the groups. From measures of amplitude, latency, and spectral power, DUP showed lower relative alpha power compared to controls. Although it is yet unclear whether 16p11.2 CNV dosage impacts neural activity in an opposing manner, findings suggest that 16p11.2 DEL impacts the level of variability of neural responses. Higher neural variability may play a role in a range of cognitive processes in 16p11.2 CNV carriers. Autism Res 2019, 12: 1322-1333. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The study analyzed the consistency of patterns of brain waves and rhythms in those affected with a loss or gain of DNA material in the 16p11.2 region. Compared with typical individuals, 16p11.2 deletion carriers showed greater inconsistency in the way the brain responds to the same visual event. This high inconsistency in brain activity may play a role in some core symptoms in 16p11.2 copy number variation carriers.

Neural Variability Limits Adolescent Skill Learning.

Skill learning is fundamental to the acquisition of many complex behaviors that emerge during development. For example, years of practice give rise to perceptual improvements that contribute to mature speech and language skills. While fully honed learning skills might be thought to offer an advantage during the juvenile period, the ability to learn actually continues to develop through childhood and adolescence, suggesting that the neural mechanisms that support skill learning are slow to mature. To address this issue, we asked whether the rate and magnitude of perceptual learning varies as a function of age as male and female gerbils trained on an auditory task. Adolescents displayed a slower rate of perceptual learning compared with their young and mature counterparts. We recorded auditory cortical neuron activity from a subset of adolescent and adult gerbils as they underwent perceptual training. While training enhanced the sensitivity of most adult units, the sensitivity of many adolescent units remained unchanged, or even declined across training days. Therefore, the average rate of cortical improvement was significantly slower in adolescents compared with adults. Both smaller differences between sound-evoked response magnitudes and greater trial-to-trial response fluctuations contributed to the poorer sensitivity of individual adolescent neurons. Together, these findings suggest that elevated sensory neural variability limits adolescent skill learning.SIGNIFICANCE STATEMENT The ability to learn new skills emerges gradually as children age. This prolonged development, often lasting well into adolescence, suggests that children, teens, and adults may rely on distinct neural strategies to improve their sensory and motor capabilities. Here, we found that practice-based improvement on a sound detection task is slower in adolescent gerbils than in younger or older animals. Neural recordings made during training revealed that practice enhanced the sound sensitivity of adult cortical neurons, but had a weaker effect in adolescents. This latter finding was partially explained by the fact that adolescent neural responses were more variable than in adults. Our results suggest that one mechanistic basis of adult-like skill learning is a reduction in neural response variability.

Contributions of Intraindividual and Interindividual Differences to Multisensory Processes.

Most evidence on the neural and perceptual correlates of sensory processing derives from studies that have focused on only a single sensory modality and averaged the data from groups of participants. Although valuable, such studies ignore the substantial interindividual and intraindividual differences that are undoubtedly at play. Such variability plays an integral role in both the behavioral/perceptual realms and in the neural correlates of these processes, but substantially less is known when compared with group-averaged data. Recently, it has been shown that the presentation of stimuli from two or more sensory modalities (i.e., multisensory stimulation) not only results in the well-established performance gains but also gives rise to reductions in behavioral and neural response variability. To better understand the relationship between neural and behavioral response variability under multisensory conditions, this study investigated both behavior and brain activity in a task requiring participants to discriminate moving versus static stimuli presented in either a unisensory or multisensory context. EEG data were analyzed with respect to intraindividual and interindividual differences in RTs. The results showed that trial-by-trial variability of RTs was significantly reduced under audiovisual presentation conditions as compared with visual-only presentations across all participants. Intraindividual variability of RTs was linked to changes in correlated activity between clusters within an occipital to frontal network. In addition, interindividual variability of RTs was linked to differential recruitment of medial frontal cortices. The present findings highlight differences in the brain networks that support behavioral benefits during unisensory versus multisensory motion detection and provide an important view into the functional dynamics within neuronal networks underpinning intraindividual performance differences.