Neural variability: friend or foe?

Neural Variability Is Quenched by Attention

Attention can be subdivided into several components, including alertness and spatial attention. It is believed that the behavioral benefits of attention, such as increased accuracy and faster reaction times, are generated by an increase in neural activity and a decrease in neural variability, which enhance the signal-to-noise ratio of task-relevant neural populations. However, empirical evidence regarding attention-related changes in neural variability in humans is extremely rare. Here we used EEG to demonstrate that trial-by-trial neural variability was reduced by visual cues that modulated alertness and spatial attention. Reductions in neural variability were specific to the visual system and larger in the contralateral hemisphere of the attended visual field. Subjects with higher initial levels of neural variability and larger decreases in variability exhibited greater behavioral benefits from attentional cues. These findings demonstrate that both alertness and spatial attention modulate neural variability and highlight the importance of reducing/quenching neural variability for attaining the behavioral benefits of attention.SIGNIFICANCE STATEMENT Attention is thought to improve perception by increasing the signal-to-noise ratio of the neuronal populations that encode the attended stimulus. Signal-to-noise ratio can be enhanced by increasing neural response (signal) and/or by reducing neural variability (noise). The ability of attention to increase neural responses has been studied extensively, but the effects of attention on neural variability have rarely been examined in humans. Here, we demonstrate that modulating different components of attention, including alertness and spatial attention, reduces neural variability in humans. Furthermore, we show that subjects with larger reductions in neural variability exhibit greater behavioral benefits from attention. These results demonstrate that reduction of neural variability is a fundamental feature of attentional processes in humans with clear behavioral importance.

Thalamus exhibits less sensory variability quenching than cortex

Spiking activity exhibits a large degree of variability across identical trials, which has been shown to be significantly reduced by stimulus onset in a wide range of cortical areas. Whether similar dynamics apply to the thalamus and in particular to the pulvinar is largely unknown. Here, we examined electrophysiological recordings from two adult rhesus macaques performing a perceptual task and comparatively investigated trial-to-trial variability in higher-order thalamus (ventral and dorsal pulvinar), the lateral geniculate nucleus (LGN) and visual cortex (area V4) prior to and following the presentation of a visual stimulus. We found spiking variability during stable fixation prior to stimulus onset to be considerably lower in both pulvinar and the LGN as compared to area V4. In contrast to the prominent variability reduction in V4 upon stimulus onset, variability in the thalamic nuclei was largely unaffected by visual stimulation. There was a small but significant variability decrease in the dorsal pulvinar, but not in the ventral portion of the pulvinar, which is closely connected to visual cortices and would thus have been expected to reflect cortical response properties. This dissociation did not stem from differences in response strength or mean firing rates and indicates fundamental differences in variability quenching between thalamus and cortex.

Going beyond the mean: Intraindividual variability of cognitive performance in prodromal and early neurodegenerative disorders

OBJECTIVE

Intraindividual variability (IIV), generally defined as short-term variations in behavior, has been proposed as a sign of subtle early impairment in neurodegenerative disorders, presumably associated with the disintegration of neuronal network connectivity. We aim to provide a review of IIV as a sensitive cognitive marker in prodromal neurodegenerative disorders.

METHOD

A narrative review focusing not only on theoretical and methodological definitions, including an overview on the neural correlates of IIV, but mainly on results from population-based and clinical-based studies on the role of IIV as a reliable predictor of mild cognitive impairment (MCI) and conversion to dementia in neurodegenerative disorders, mostly Alzheimer's and Parkinson's disease.

RESULTS

Most studies focus on MCI and Alzheimer's disease and demonstrate that IIV is a reliable cognitive marker. IIV is partly more sensitive than mean performance in the prediction of cognitive impairment or progressive deterioration and is independent of socio-demographic variables and disease mediators (e.g., genetic susceptibility). Neuroimaging data, mostly from healthy subjects, suggest a relationship between IIV and dysfunction of the default mode network, presumably mediated by white matter disintegration in frontal and parietal areas.

CONCLUSIONS

IIV measures may provide valuable information about diagnosis and progression in prodromal stages of neurodegenerative disorders. Thus, further conceptual and methodological clarifications are needed to justify the inclusion of IIV as a sensible cognitive marker in routine clinical neuropsychological assessment.

Object complexity modulates the association between action and perception in childhood

Vision for action and vision for perception both rely on shape representations derived within the visual system. Whether the same psychological and neural mechanisms underlie both forms of behavior remains hotly contested, and whether this arrangement is equivalent in adults and children is controversial as well. To address these outstanding questions, we used an established psychophysical heuristic, Weber's law, which, in adults, has typically been observed for perceptual judgment tasks but not for actions such as grasping. We examined whether this perception-action dissociation in Weber's law was present in childhood as it is in adulthood and whether it was modulated by stimulus complexity. Two major results emerged. First, although adults evinced visuomotor behavior that violated Weber's law, young children (4.5-6.5 years) adhered to Weber's law when they grasped complex objects ("Efron" blocks), which varied along both the graspable and non-graspable dimensions to maintain a constant surface area, but not when they grasped simple objects, which varied only along the graspable dimension. Second, adherence to Weber's law was found across all ages in the context of a perceptual task. Together, these findings suggest that, in early childhood, visuomotor representations are modulated by perceptual representations, particularly when a refined description of object shape is needed.

Interhemispheric alpha-band hypoconnectivity in children with autism spectrum disorder

Diverse genetic and environmental etiologies converge onto circuit level brain dysfunction in autism spectrum disorder (ASD), manifesting at a macroscopic level as aberrant neural connectivity. Previous studies have described atypical patterns of decreased short range and increased long range connectivity in ASD [1 ]. However, it remains unclear whether group level features of circuit dysfunction are consistently present across the range of cognitive function seen in the autism spectrum.

The dynamics of neural oscillations in the alpha range (6-12 Hz) are exquisitely sensitive to healthy development of functional and structural connectivity. Alpha-band coherence, measured with high temporal-precision electroencephalography (EEG) therefore represents an ideal tool for studying neural connectivity in developmental populations.

Here we examined spontaneous alpha phase coherence in a heterogeneous sample of 59 children with ASD and 39 age matched typically developing children. Using a data driven approach, we conducted an unbiased examination of all possible atypical connectivity patterns across all cortical regions.

Long-range hypoconnectivity was present in children with ASD compared to typically developing children, with temporal interhemispheric connectivity showing the largest difference between the two groups. Decreased long range alpha coherence distinguishes a heterogeneous group of ASD children from typically developing children. Interhemispheric temporal hypoconnectivity represents a fundamental functional difference in children with ASD across a wide cognitive and age range that may reflect white matter disturbances or increased signal variability at temporal sites in ASD.

Meta-analytic findings reveal lower means but higher variances in visuospatial ability in dyslexia

Conflicting empirical and theoretical accounts suggest that dyslexia is associated with either average, enhanced, or impoverished high-level visuospatial processing relative to controls. Such heterogeneous results could be due to the presence of wider variability in dyslexic samples, which is unlikely to be identified at the single study level, due to lack of power. To address this, this study reports a meta-analysis of means and variances in high-level visuospatial ability in 909 non-dyslexic and 956 dyslexic individuals. The findings suggest that dyslexia is associated not only with a lower mean performance on visuospatial tasks but also with greater variability in performance. Through novel meta-analytic techniques, we demonstrate a negative effect size for mean differences (-.457), but a positive effect size for SD differences (+.118; SD ratio = 1.107). In doing so, this is the first study to demonstrate impoverished visuospatial processing of the majority of individuals with dyslexia in addition to greater variance in performance in this group. The findings advocate for further consideration of both the presence of, and reasons for, increased variance in perception, attention, and memory across neurodevelopmental disorders.

Symptoms of ADHD Affect Intrasubject Variability in Youths with Autism Spectrum Disorder: An Ex-Gaussian Analysis

Increased intrasubject variability in reaction times (RT-ISV) is frequently found in individuals with autism spectrum disorder (ASD). However, how dimensional attention deficit/hyperactivity disorder (ADHD) symptoms impact RT-ISV in individuals with ASD remains elusive. We assessed 97 high-functioning youths with co-occurring ASD and ADHD (ASD+ADHD), 124 high-functioning youths with ASD only, 98 youths with ADHD only, and 249 typically developing youths, 8-18 years of age, using the Conners Continuous Performance Test (CCPT). We compared the conventional CCPT parameters (omission errors, commission errors, mean RT and RT standard error (RTSE) as well as the ex-Gaussian parameters of RT (mu, sigma, and tau) across the four groups. We also conducted regression analyses to assess the relationships between RT indices and symptoms of ADHD and ASD in the ASD group (i.e., the ASD+ADHD and ASD-only groups). The ASD+ADHD and ADHD-only groups had higher RT-ISV than the other two groups. RT-ISV, specifically RTSE and tau, was significantly associated with ADHD symptoms rather than autistic traits in the ASD group. Regression models also revealed that sex partly accounted for RT-ISV variance in the ASD group. A post hoc analysis showed girls with ASD had higher tau and RTSE values than their male counterparts. Our results suggest that RT-ISV is primarily associated with co-occurring ADHD symptoms/diagnosis in children and adolescents with ASD. These results do not support the hypothesis of response variability as a transdiagnostic phenotype for ASD and ADHD and warrant further validation at a neural level.

Atypical audiovisual temporal function in autism and schizophrenia: similar phenotype, different cause

Binding across sensory modalities yields substantial perceptual benefits, including enhanced speech intelligibility. The coincidence of sensory inputs across time is a fundamental cue for this integration process. Recent work has suggested that individuals with diagnoses of schizophrenia (SZ) and autism spectrum disorder (ASD) will characterize auditory and visual events as synchronous over larger temporal disparities than their neurotypical counterparts. Namely, these clinical populations possess an enlarged temporal binding window (TBW). Although patients with SZ and ASD share aspects of their symptomatology, phenotypic similarities may result from distinct etiologies. To examine similarities and variances in audiovisual temporal function in these two populations, individuals diagnosed with ASD (n = 46; controls n = 40) and SZ (n = 16, controls = 16) completed an audiovisual simultaneity judgment task. In addition to standard psychometric analyses, synchrony judgments were assessed using Bayesian causal inference modeling. This approach permits distinguishing between distinct causes of an enlarged TBW: an a priori bias to bind sensory information and poor fidelity in the sensory representation. Findings indicate that both ASD and SZ populations show deficits in multisensory temporal acuity. Importantly, results suggest that while the wider TBWs in ASD most prominently results from atypical priors, the wider TBWs in SZ results from a trend toward changes in prior and weaknesses in the sensory representations. Results are discussed in light of current ASD and SZ theories and highlight that different perceptual training paradigms focused on improving multisensory integration may be most effective in these two clinical populations and emphasize that similar phenotypes may emanate from distinct mechanistic causes.

Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick

Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks. This clustered topology introduces multistability and winnerless competition between attractors and can capture the high trial-to-trial variability and its reduction during stimulation that has been found experimentally. In this prospect article, we review the mean field description of balanced networks of binary neurons and apply the theory to clustered networks. We show that the stable fixed points of networks with clustered excitatory connectivity tend quickly towards firing rate saturation, which is generally inconsistent with experimental data. To remedy this shortcoming, we then present a novel perspective on networks with locally balanced clusters of both excitatory and inhibitory neuron populations. This approach allows for true multistability and moderate firing rates in activated clusters over a wide range of parameters. Our findings are supported by mean field theory and numerical network simulations. Finally, we discuss possible applications of the concept of joint excitatory and inhibitory clustering in future cortical network modelling studies.

A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection

Inter- and intra-subject variability pose a major challenge to decoding human brain activity in brain-computer interfaces (BCIs) based on non-invasive electroencephalogram (EEG). Conventionally, a time-consuming and laborious training procedure is performed on each new user to collect sufficient individualized data, hindering the applications of BCIs on monitoring brain states (e.g. drowsiness) in real-world settings. This study proposes applying hierarchical clustering to assess the inter- and intra-subject variability within a large-scale dataset of EEG collected in a simulated driving task, and validates the feasibility of transferring EEG-based drowsiness-detection models across subjects. A subject-transfer framework is thus developed for detecting drowsiness based on a large-scale model pool from other subjects and a small amount of alert baseline calibration data from a new user. The model pool ensures the availability of positive model transferring, whereas the alert baseline data serve as a selector of decoding models in the pool. Compared with the conventional within-subject approach, the proposed framework remarkably reduced the required calibration time for a new user by 90% (18.00 min-1.72 ± 0.36 min) without compromising performance (p = 0.0910) when sufficient existing data are available. These findings suggest a practical pathway toward plug-and-play drowsiness detection and can ignite numerous real-world BCI applications.

Resting-State Brain Signal Variability in Prefrontal Cortex Is Associated With ADHD Symptom Severity in Children

Atypical brain function in attention-deficit/hyperactivity disorder (ADHD) has been identified using both task-activation and functional connectivity fMRI approaches. Recent work highlights the potential for another measure derived from functional neuroimaging data, brain signal variability, to reveal insights into clinical conditions. Higher brain signal variability has previously been linked with optimal behavioral performance. At present, little is known regarding the relationship between resting-state brain signal variability and ADHD symptom severity. The current study examined the relationship between a measure of moment-to-moment brain signal variability called mean-square successive difference (MSSD) and ADHD symptomatology in a group of children (7–12 years old) with (n = 40) and without (n = 30) a formal diagnosis of ADHD. A categorical analysis comparing subjects with and without a clinical diagnosis of ADHD showed no differences in MSSD between groups. A dimensional analysis revealed a positive relationship between MSSD and overall ADHD symptom severity and inattention across children with and without an ADHD diagnosis. Specifically, this positive relationship was found in medial prefrontal areas comprising the default mode network. These results demonstrate a link between intrinsic brain signal variability and ADHD symptom severity that cuts across diagnostic categories, and point to a locus of dysfunction consistent with previous neuroimaging literature.

An Examination of the Neural Unreliability Thesis of Autism

An emerging neuropathological theory of Autism, referred to here as “the neural unreliability thesis,” proposes greater variability in moment-to-moment cortical representation of environmental events, such that the system shows general instability in its impulse response function. Leading evidence for this thesis derives from functional neuroimaging, a methodology ill-suited for detailed assessment of sensory transmission dynamics occurring at the millisecond scale. Electrophysiological assessments of this thesis, however, are sparse and unconvincing. We conducted detailed examination of visual and somatosensory evoked activity using high-density electrical mapping in individuals with autism (N = 20) and precisely matched neurotypical controls (N = 20), recording large numbers of trials that allowed for exhaustive time-frequency analyses at the single-trial level. Measures of intertrial coherence and event-related spectral perturbation revealed no convincing evidence for an unreliability account of sensory responsivity in autism. Indeed, results point to robust, highly reproducible response functions marked for their exceedingly close correspondence to those in neurotypical controls

The Variability of Neural Responses to Naturalistic Videos Change with Age and Sex

Neural development is generally marked by an increase in the efficiency and diversity of neural processes. In a large sample (n = 114) of human children and adults with ages ranging from 5 to 44 yr, we investigated the neural responses to naturalistic video stimuli. Videos from both real-life classroom settings and Hollywood feature films were used to probe different aspects of attention and engagement. For all stimuli, older ages were marked by more variable neural responses. Variability was assessed by the intersubject correlation of evoked electroencephalographic responses. Young males also had less-variable responses than young females. These results were replicated in an independent cohort (n = 303). When interpreted in the context of neural maturation, we conclude that neural function becomes more variable with maturity, at least during the passive viewing of real-world stimuli.

The Magnitude of Trial-By-Trial Neural Variability Is Reproducible over Time and across Tasks in Humans

Numerous studies have shown that neural activity in sensory cortices is remarkably variable over time and across trials even when subjects are presented with an identical repeating stimulus or task. This trial-by-trial neural variability is relatively large in the prestimulus period and considerably smaller (quenched) following stimulus presentation. Previous studies have suggested that the magnitude of neural variability affects behavior such that perceptual performance is better on trials and in individuals where variability quenching is larger. To what degree are neural variability magnitudes of individual subjects flexible or static? Here, we used EEG recordings from adult humans to demonstrate that neural variability magnitudes in visual cortex are remarkably consistent across different tasks and recording sessions. While magnitudes of neural variability differed dramatically across individual subjects, they were surprisingly stable across four tasks with different stimuli, temporal structures, and attentional/cognitive demands as well as across experimental sessions separated by one year. These experiments reveal that, in adults, neural variability magnitudes are mostly solidified individual characteristics that change little with task or time, and are likely to predispose individual subjects to exhibit distinct behavioral capabilities.

High internal noise and poor external noise filtering characterize perception in autism spectrum disorder

An emerging hypothesis postulates that internal noise is a key factor influencing perceptual abilities in autism spectrum disorder (ASD). Given fundamental and inescapable effects of noise on nearly all aspects of neural processing, this could be a critical abnormality with broad implications for perception, behavior, and cognition. However, this proposal has been challenged by both theoretical and empirical studies. A crucial question is whether and how internal noise limits perception in ASD, independently from other sources of perceptual inefficiency, such as the ability to filter out external noise. Here, we separately estimated internal noise and external noise filtering in ASD. In children and adolescents with and without ASD, we computationally modeled individuals' visual orientation discrimination in the presence of varying levels of external noise. The results revealed increased internal noise and worse external noise filtering in individuals with ASD. For both factors, we also observed high inter-individual variability in ASD, with only the internal noise estimates significantly correlating with severity of ASD symptoms. We provide evidence for reduced perceptual efficiency in ASD that is due to both increased internal noise and worse external noise filtering, while highlighting internal noise as a possible contributing factor to variability in ASD symptoms.

States and traits of neural irregularity in the age-varying human brain

Sensory representations, and thus human percepts, of the physical world are susceptible to fluctuations in brain state or “neural irregularity”. Furthermore, aging brains display altered levels of neural irregularity. We here show that a single, within-trial, information-theoretic measure (weighted permutation entropy) captures neural irregularity in the human electroencephalogram as a proxy for both, trait-like differences between individuals of varying age, and state-like fluctuations that bias perceptual decisions. First, the overall level of neural irregularity increased with participants’ age, paralleled by a decrease in variability over time, likely indexing age-related changes at structural and functional levels of brain activity. Second, states of higher neural irregularity were associated with optimized sensory encoding and a subsequently increased probability of choosing the first of two physically identical stimuli to be higher in pitch. In sum, neural irregularity not only characterizes behaviourally relevant brain states, but also can identify trait-like changes that come with age.

Epilepsy as a Network Disorder (1): What can we learn from other network disorders such as autistic spectrum disorder and mood disorders?

Epilepsy is a neurologic condition which often occurs with other neurologic and psychiatric disorders. The relation between epilepsy and these conditions is complex. Some population-based studies have identified a bidirectional relation, whereby not only patients with epilepsy are at increased risk of suffering from some of these neurologic and psychiatric disorders (migraine, stroke, dementia, autism, depression, anxiety disorders, Attention deficit hyperactivity disorder (ADHD), and psychosis), but also patients with these conditions are at increased risk of suffering from epilepsy. The existence of common pathogenic mechanisms has been postulated as a potential explanation of this phenomenon. To reassess the relationships between neurological and psychiatric conditions in general, and specifically autism, depression, Alzheimer's disease, schizophrenia, and epilepsy, a recent meeting brought together basic researchers and clinician scientists entitled "Epilepsy as a Network Disorder." This was the fourth in a series of conferences, the "Fourth International Halifax Conference and Retreat". This manuscript summarizes the proceedings on potential relations between Epilepsy on the one hand and autism and depression on the other. A companion manuscript provides a summary of the proceedings about the relation between epilepsy and Alzheimer's disease and schizophrenia, closed by the role of translational research in clarifying these relationships. The review of the topics in these two manuscripts will provide a better understanding of the mechanisms operant in some of the common neurologic and psychiatric comorbidities of epilepsy.

The equivalent internal orientation and position noise for contour integration

Contour integration is the joining-up of local responses to parts of a contour into a continuous percept. In typical studies observers detect contours formed of discrete wavelets, presented against a background of random wavelets. This measures performance for detecting contours in the limiting external noise that background provides. Our novel task measures contour integration without requiring any background noise. This allowed us to perform noise-masking experiments using orientation and position noise. From these we measure the equivalent internal noise for contour integration. We found an orientation noise of 6° and position noise of 3 arcmin. Orientation noise was 2.6x higher in contour integration compared to an orientation discrimination control task. Comparing against a position discrimination task found position noise in contours to be 2.4x lower. This suggests contour integration involves intermediate processing that enhances the quality of element position representation at the expense of element orientation. Efficiency relative to the ideal observer was lower for the contour tasks (36% in orientation noise, 21% in position noise) compared to the controls (54% and 57%).

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.

Individual Movement Variability Magnitudes Are Explained by Cortical Neural Variability

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior-parietal cortex of individual subjects explained their movement-extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities.SIGNIFICANCE STATEMENT Neural activity and movement kinematics are remarkably variable. Although intertrial variability is rarely studied, here, we demonstrate that individual human subjects exhibit distinct magnitudes of neural and kinematic variability that are reproducible across movements to different targets and when performing these movements with either arm. Furthermore, when examining the relationship between cortical variability and movement variability, we find that cortical fMRI variability in parietal cortex of individual subjects explained their movement extent variability. This enabled us to explain why some subjects performed more variable movements than others based on their cortical variability magnitudes.

Motor errors lead to enhanced performance in older adults

Young individuals make larger and faster forearm movements when visual feedback about the movement is not available, compared to when it is. We set out to test whether this behavior persists with aging. We tested 40 participants, 20 in each age group - young and old, on a task that required making rhythmic movements of the forearm with and without visual feedback. Surprisingly, we found that older adults increased the speed and the amplitude of their movements to an even greater extent than did the young adults. Furthermore, we found that the increase in speed and amplitude during the non-vision trial segments improved their performance on the task, and they were able to leverage the change in these movement parameters (speed and amplitude) to improve their performance during subsequent trial segments that did include visual feedback. The improvement in accuracy on the task was accompanied by a decrease in path variability. The results indicate that older adults can adapt their movement parameters to enhance performance following a motor perturbation. They further suggest that motor variability in old age can be advantageous under certain circumstances.

Vision as a Beachhead

When neural circuits develop abnormally due to different genetic deficits and/or environmental insults, neural computations and the behaviors that rely on them are altered. Computational theories that relate neural circuits with specific quantifiable behavioral and physiological phenomena, therefore, serve as extremely useful tools for elucidating the neuropathological mechanisms that underlie different disorders. The visual system is particularly well suited for characterizing differences in neural computations; computational theories of vision are well established, and empirical protocols for measuring the parameters of those theories are well developed. In this article, we examine how psychophysical and neuroimaging measurements from human subjects are being used to test hypotheses about abnormal neural computations in autism, with an emphasis on hypotheses regarding potential excitation/inhibition imbalances. We discuss the complexity of relating specific computational abnormalities to particular underlying mechanisms given the diversity of neural circuits that can generate the same computation, and we discuss areas of research in which computational theories need to be further developed to provide useful frameworks for interpreting existing results. A final emphasis is placed on the need to extend existing ideas into developmental frameworks that take into account the dramatic developmental changes in neurophysiology (e.g., changes in excitation/inhibition balance) that take place during the first years of life, when autism initially emerges.

Internal noise estimates correlate with autistic traits

Previous neuroimaging research has reported increased internal (neural) noise in sensory systems of autistic individuals. However, it is unclear if this difference has behavioural or perceptual consequences, as previous attempts at measuring internal noise in ASD psychophysically have been indirect. Here, we use a "gold standard" psychophysical double-pass paradigm to investigate the relationship between internal noise and autistic traits in the neurotypical population (n = 43). We measured internal noise in three tasks (contrast perception, facial expression intensity perception, and number summation) to estimate a global internal noise factor using principal components analysis. This global internal noise was positively correlated with autistic traits (r_s  = 0.32, P = 0.035). This suggests that increased internal noise is associated with the ASD phenotype even in subclinical populations. The finding is discussed in relation to the neural and genetic basis of internal noise in ASD. Autism Res 2017, 10: 1384-1391. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Intra-individual cortisol variability and low-grade inflammation over 10 years in older adults

OBJECTIVE

This study examined the associations between intra-individual variability in, and inter-individual levels of, diurnal cortisol secretion with a marker of low-grade inflammation (i.e., C-Reactive Protein; CRP). Reasoning that greater day-to-day cortisol variability could reflect a dysregulation of the HPA axis, we hypothesized that it would predict higher levels of CRP, above and beyond inter-individual differences in cortisol levels.

METHODS

A 10-year longitudinal study of 130 older adults examined diurnal cortisol secretion on three different days across each of the 6 waves of data collection and levels of CRP during the last 3 waves. Indicators of mean cortisol levels, short-term cortisol variability, and long-term cortisol variability were analyzed.

RESULTS

Hierarchical linear modeling showed significant main effects, linking baseline mean cortisol levels, T-ratio=2.25, p=0.03, and long-term cortisol variability, T-ratio=2.63, p=0.01, with higher CRP values six to ten years after study entry. In addition, a two-way interaction demonstrated that short-term variability in cortisol were associated with higher levels of CRP among individuals who secreted relatively high, T-ratio=2.68, p=0.01, but not low, T-ratio=-1.09, p=0.28, baseline levels of cortisol. Finally, a three-way interaction, T-ratio=2.24, p=0.03, suggested that the effect of long-term cortisol variability on CRP became stronger over time among participants who secreted high average levels of cortisol, whereas it became weaker among their counterparts who secreted low average levels of cortisol.

CONCLUSION

Variability in cortisol secretion across days forecasts low-grade inflammation, and this association is paramount among older adults who secrete high levels of diurnal cortisol.

Theory of cortical function

Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction.

Pace Yourself: Intraindividual Variability in Context Use Revealed by Self-paced Event-related Brain Potentials

Event-related brain potentials (ERPs) have revealed multiple mechanisms by which contextual constraints impact language processing. At the same time, little work has examined the trial-to-trial dynamics of context use in the brain. In the current study, we probed intraindividual variability in behavioral and neural indices of context processing during reading. In a concurrent self-paced reading and ERP paradigm, participants read sentences that were either strongly or weakly constraining completed with an expected or unexpected target word. Our findings revealed substantial within-subject variability in behavioral and neural responses to contextual constraints. First, context-based amplitude reductions of the N400, a component linked to semantic memory access, were largest among trials eliciting the slowest RTs. Second, the RT distribution of unexpected words in strongly constraining contexts was positively skewed, reflecting an increased proportion of very slow RTs to trials that violated semantic predictions. Among those prediction-violating trials eliciting faster RTs, a late sustained anterior positivity was observed. However, among trials producing the differentially slowed RTs to prediction violations, we observed a markedly earlier effect of constraint in the form of an anterior N2, a component linked to conflict resolution and the cognitive control of behavior. The current study provides the first neurophysiological evidence for the direct role of cognitive control functions in the volitional control of reading. Collectively, our findings suggest that context use varies substantially within individual participants and that coregistering behavioral and neural indices of online sentence processing offers a window into these single-item dynamics.

Neural Variability Quenching Predicts Individual Perceptual Abilities

Neural activity during repeated presentations of a sensory stimulus exhibits considerable trial-by-trial variability. Previous studies have reported that trial-by-trial neural variability is reduced (quenched) by the presentation of a stimulus. However, the functional significance and behavioral relevance of variability quenching and the potential physiological mechanisms that may drive it have been studied only rarely. Here, we recorded neural activity with EEG as subjects performed a two-interval forced-choice contrast discrimination task. Trial-by-trial neural variability was quenched by ∼40% after the presentation of the stimulus relative to the variability apparent before stimulus presentation, yet there were large differences in the magnitude of variability quenching across subjects. Individual magnitudes of quenching predicted individual discrimination capabilities such that subjects who exhibited larger quenching had smaller contrast discrimination thresholds and steeper psychometric function slopes. Furthermore, the magnitude of variability quenching was strongly correlated with a reduction in broadband EEG power after stimulus presentation. Our results suggest that neural variability quenching is achieved by reducing the amplitude of broadband neural oscillations after sensory input, which yields relatively more reproducible cortical activity across trials and enables superior perceptual abilities in individuals who quench more.

SIGNIFICANCE STATEMENT

Variability quenching is a phenomenon in which neural variability across trials is reduced by the presentation of a stimulus. Although this phenomenon has been reported across a variety of animal and human studies, its functional significance and behavioral relevance have been examined only rarely. Here, we report novel empirical evidence from humans revealing that variability quenching differs dramatically across individual subjects and explains to a certain degree why some individuals exhibit better perceptual abilities than others. In addition, we found a strong relationship between variability quenching and suppression of broadband neural oscillations. Together, our results reveal the importance of reproducible cortical activity for enabling better perceptual abilities and suggest a potential underlying mechanism that may explain why variability quenching occurs.

Similarities in Autistic and Neurotypical Visual–Haptic Perception When Making Judgements About Conflicting Sensory Stimuli

A number of studies have shown that multisensory performance is well predicted by a statistically optimal maximum likelihood estimation (MLE) model. Under this model unisensory estimates are combined additively and weighted according to relative reliability. Recent theories have proposed that atypical sensation and perception commonly reported in autism spectrum condition (ASC) may result from differences in the use of reliability information. Furthermore, experimental studies have indicated that multisensory processing is less effective in those with the condition in comparison to neurotypical (NT) controls. In the present study, adults with ASC () and a matched NT group () completed a visual–haptic size judgement task (cf. Gori et al., 2008) in which participants compared the height of wooden blocks using either vision or haptics, and in a dual modality condition in which visual–haptic stimuli were presented in size conflict. Participants with ASC tended to produce more reliable estimates than the NT group. However, dual modality performance was not well predicted by the MLE model for either group. Performance was subsequently compared to alternative models in which the participant either switched between modalities trial to trial (rather than integrating) and a model of non-optimal integration. Performance of both groups was statistically comparable to the cue-switching model. These findings suggest that adults with ASC adopted a similar strategy to NTs when processing conflicting visual–haptic information. Findings are discussed in relation to multisensory perception in ASC and methodological considerations associated with multisensory conflict paradigms.

Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism

HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), "genuine harmful" (noise), "genuine neutral" (synonyms, repeats), and "genuine useful" (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.

To work or not to work: Neural representation of cost and benefit of instrumental action

By definition, instrumental actions are performed in order to obtain certain goals. Nevertheless, the attainment of goals typically implies obstacles, and response vigor is known to reflect an integration of subjective benefit and cost. Whereas several brain regions have been associated with cost/benefit ratio decision-making, trial-by-trial fluctuations in motivation are not well understood. We review recent evidence supporting the motivational implications of signal fluctuations in the mesocorticolimbic system. As an extension of "set-point" theories of instrumental action, we propose that response vigor is determined by a rapid integration of brain signals that reflect value and cost on a trial-by-trial basis giving rise to an online estimate of utility. Critically, we posit that fluctuations in key nodes of the network can predict deviations in response vigor and that variability in instrumental behavior can be accounted for by models devised from optimal control theory, which incorporate the effortful control of noise. Notwithstanding, the post hoc analysis of signaling dynamics has caveats that can effectively be addressed in future research with the help of two novel fMRI imaging techniques. First, adaptive fMRI paradigms can be used to establish a time-order relationship, which is a prerequisite for causality, by using observed signal fluctuations as triggers for stimulus presentation. Second, real-time fMRI neurofeedback can be employed to induce predefined brain states that may facilitate benefit or cost aspects of instrumental actions. Ultimately, understanding temporal dynamics in brain networks subserving response vigor holds the promise for targeted interventions that could help to readjust the motivational balance of behavior.

Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case-control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu.

Differential sensory fMRI signatures in autism and schizophrenia: Analysis of amplitude and trial-to-trial variability

Autism and schizophrenia share multiple phenotypic and genotypic markers, and there is ongoing debate regarding the relationship of these two disorders. To examine whether cortical dynamics are similar across these disorders, we directly compared fMRI responses to visual, somatosensory and auditory stimuli in adults with autism (N=15), with schizophrenia (N=15), and matched controls (N=15). All participants completed a one-back letter detection task presented at fixation (to control attention) while task-irrelevant sensory stimulation was delivered to the different modalities. We focused specifically on the response amplitudes and the variability in sensory fMRI responses of the two groups, given the evidence of greater trial-to-trial variability in adults with autism. Both autism and schizophrenia individuals showed weaker signal-to-noise ratios (SNR) in sensory-evoked responses compared to controls (d>0.42), but for different reasons. For the autism group, the fMRI response amplitudes were indistinguishable from controls but were more variable trial-to-trial (d=0.47). For the schizophrenia group, response amplitudes were smaller compared to autism (d=0.44) and control groups (d=0.74), but were not significantly more variable (d<0.29). These differential group profiles suggest (1) that greater trial-to-trial variability in cortical responses may be specific to autism and is not a defining characteristic of schizophrenia, and (2) that blunted response amplitudes may be characteristic of schizophrenia. The relationship between the amplitude and the variability of cortical activity might serve as a specific signature differentiating these neurodevelopmental disorders. Identifying the neural basis of these responses and their relationship to the underlying genetic bases may substantially enlighten the understanding of both disorders.

Toward an Interdisciplinary Understanding of Sensory Dysfunction in Autism Spectrum Disorder: An Integration of the Neural and Symptom Literatures

Sensory processing differences have long been associated with autism spectrum disorder (ASD), and they have recently been added to the diagnostic criteria for the disorder. The focus on sensory processing in ASD research has increased substantially in the last decade. This research has been approached from two different perspectives: the first focuses on characterizing the symptoms that manifest in response to real world sensory stimulation, and the second focuses on the neural pathways and mechanisms underlying sensory processing. The purpose of this paper is to integrate the empirical literature on sensory processing in ASD from the last decade, including both studies characterizing sensory symptoms and those that investigate neural response to sensory stimuli. We begin with a discussion of definitions to clarify some of the inconsistencies in terminology that currently exist in the field. Next, the sensory symptoms literature is reviewed with a particular focus on developmental considerations and the relationship of sensory symptoms to other core features of the disorder. Then, the neuroscience literature is reviewed with a focus on methodological approaches and specific sensory modalities. Currently, these sensory symptoms and neuroscience perspectives are largely developing independently from each other leading to multiple, but separate, theories and methods, thus creating a multidisciplinary approach to sensory processing in ASD. In order to progress our understanding of sensory processing in ASD, it is now critical to integrate these two research perspectives and move toward an interdisciplinary approach. This will inevitably aid in a better understanding of the underlying biological basis of these symptoms and help realize the translational value through its application to early identification and treatment. The review ends with specific recommendations for future research to help bridge these two research perspectives in order to advance our understanding of sensory processing in ASD.

Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience

Cognitive flexibility, the readiness with which one can selectively switch between mental processes to generate appropriate behavioral responses, develops in a protracted manner and is compromised in several prevalent neurodevelopmental disorders. It is unclear whether cognitive flexibility arises from neural substrates distinct from the executive control network (ECN) or from the interplay of nodes within this and other networks. Here we review neuroimaging studies of cognitive flexibility, focusing on set shifting and task switching. We propose that more consistent operationalization and study of cognitive flexibility is required in clinical and developmental neuroscience. We suggest that an important avenue for future research is the characterization of the relationship between neural flexibility and cognitive flexibility in typical and atypical development.

Increased ongoing neural variability in ADHD

Attention Deficit Hyperactivity Disorder (ADHD) has been described as a disorder where frequent lapses of attention impair the ability of an individual to focus/attend in a sustained manner, thereby generating abnormally large intra-individual behavioral variability across trials. Indeed, increased reaction time (RT) variability is a fundamental behavioral characteristic of individuals with ADHD found across a large number of cognitive tasks. But what is the underlying neurophysiology that might generate such behavioral instability? Here, we examined trial-by-trial EEG response variability to visual and auditory stimuli while subjects' attention was diverted to an unrelated task at the fixation cross. Comparisons between adult ADHD and control participants revealed that neural response variability was significantly larger in the ADHD group as compared with the control group in both sensory modalities. Importantly, larger trial-by-trial variability in ADHD was apparent before and after stimulus presentation as well as in trials where the stimulus was omitted, suggesting that ongoing (rather than stimulus-evoked) neural activity is continuously more variable (noisier) in ADHD. While the patho-physiological mechanisms causing this increased neural variability remain unknown, they appear to act continuously rather than being tied to a specific sensory or cognitive process.

Intertrial auditory neural stability supports beat synchronization in preschoolers

The ability to synchronize motor movements along with an auditory beat places stringent demands on the temporal processing and sensorimotor integration capabilities of the nervous system. Links between millisecond-level precision of auditory processing and the consistency of sensorimotor beat synchronization implicate fine auditory neural timing as a mechanism for forming stable internal representations of, and behavioral reactions to, sound. Here, for the first time, we demonstrate a systematic relationship between consistency of beat synchronization and trial-by-trial stability of subcortical speech processing in preschoolers (ages 3 and 4 years old). We conclude that beat synchronization might provide a useful window into millisecond-level neural precision for encoding sound in early childhood, when speech processing is especially important for language acquisition and development.

Single-Cell Memory Regulates a Neural Circuit for Sensory Behavior

Unveiling the molecular and cellular mechanisms underlying memory has been a challenge for the past few decades. Although synaptic plasticity is proven to be essential for memory formation, the significance of "single-cell memory" still remains elusive. Here, we exploited a primary culture system for the analysis of C. elegans neurons and show that a single thermosensory neuron has an ability to form, retain, and reset a temperature memory. Genetic and proteomic analyses found that the expression of the single-cell memory exhibits inter-individual variability, which is controlled by the evolutionarily conserved CaMKI/IV and Raf pathway. The variable responses of a sensory neuron influenced the neural activity of downstream interneurons, suggesting that modulation of the sensory neurons ultimately determines the behavioral output in C. elegans. Our results provide proof of single-cell memory and suggest that the individual differences in neural responses at the single-cell level can confer individuality.

Kaleidoscope

Antidepressant effectiveness is a topic seldom out of the scientific or popular press, with claim and counter-claim about the disclosure, reporting, and interpretation of data. Hieronymus and colleagues1 have thrown their hats into the ring: noting that about half of company-sponsored trials failed to show any superiority over placebo, they challenge that most studies evaluated changes in total scores on the 17-point Hamilton Rating Scale for Depression (HRSD-17), but that this might mask improvement in important subcomponents. Not all items equally correlate with illness burden, so they undertook patient-level post hoc analyses focusing on the four-point depressed mood subcomponent in 18 placebo-controlled industry trials of various selective serotonin reuptake inhibitors (n = 6669). The choice of this question was based on its diagnostic importance and the fact it had the highest baseline severity in the pooled sample. The result was that 91% of comparisons showed superiority of the active drug over placebo, compared with 46% where the summed scale was used (P<0.001). The authors argue that the summed scale is insensitive, and clouds current views on medications. The end of the debate? We think not …

The Nature of Shared Cortical Variability

Neuronal responses of sensory cortex are highly variable, and this variability is correlated across neurons. To assess how variability reflects factors shared across a neuronal population, we analyzed the activity of many simultaneously recorded neurons in visual cortex. We developed a simple model that comprises two sources of shared variability: a multiplicative gain, which uniformly scales each neuron’s sensory drive, and an additive offset, which affects different neurons to different degrees. This model captured the variability of spike counts and reproduced the dependence of pairwise correlations on neuronal tuning and stimulus orientation. The relative contributions of the additive and multiplicative fluctuations could vary over time and had marked impact on population coding. These observations indicate that shared variability of neuronal populations in sensory cortex can be largely explained by two factors that modulate the whole population.

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Response variability in V1 neuronal populations is largely shared across neurons

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Shared variability involves two factors: a multiplicative gain and an additive offset

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These two factors predict sensory responses of large populations on single trials

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They determine pairwise correlations and constrain information coding