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

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

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

A measure centrality index for systematic empirical comparison of consciousness theories

Consciousness science is marred by disparate constructs and methodologies, making it challenging to systematically compare theories. This foundational crisis casts doubts on the scientific character of the field itself. Addressing it, we propose a framework for systematically comparing consciousness theories by introducing a novel inter-theory classification interface, the Measure Centrality Index (MCI). Recognizing its gradient distribution, the MCI assesses the degree of importance a specific empirical measure has for a given consciousness theory. We apply the MCI to probe how the empirical measures of the Global Neuronal Workspace Theory (GNW), Integrated Information Theory (IIT), and Temporospatial Theory of Consciousness (TTC) would fare within the context of the other two. We demonstrate that direct comparison of IIT, GNW, and TTC is meaningful and valid for some measures like Lempel-Ziv Complexity (LZC), Autocorrelation Window (ACW), and possibly Mutual Information (MI). In contrast, it is problematic for others like the anatomical and physiological neural correlates of consciousness (NCC) due to their MCI-based differential weightings within the structure of the theories. In sum, we introduce and provide proof-of-principle of a novel systematic method for direct inter-theory empirical comparisons, thereby addressing isolated evolution of theories and confirmatory bias issues in the state-of-the-art neuroscience of consciousness.

From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.

Longitudinal stability of individual brain plasticity patterns in blindness

The primary visual cortex (V1) in individuals born blind is engaged in a wide spectrum of tasks and sensory modalities, including audition, touch, language, and memory. This widespread involvement raises questions regarding the constancy of its role and whether it might exhibit flexibility in its function over time, connecting to diverse network functions in response to task-specific demands. This would suggest that reorganized V1 takes on a role similar to cognitive multiple-demand system regions. Alternatively, it is possible that the varying patterns of plasticity observed in the blind V1 can be attributed to individual factors, whereby different blind individuals recruit V1 for different functions, highlighting the immense idiosyncrasy of plasticity. In support of this second account, we have recently shown that V1 functional connectivity varies greatly across blind individuals. But do these represent stable individual patterns of plasticity or merely instantaneous changes, for a multiple-demand system now inhabiting V1? Here we tested if individual connectivity patterns from the visual cortex of blind individuals are stable over time. We show that over two years, fMRI functional connectivity from the primary visual cortex is unique and highly stable in a small sample of repeatedly sampled congenitally blind individuals. Further, using multivoxel pattern analysis, we demonstrate that the unique reorganization patterns of these individuals allow decoding of participant identity. Together with recent evidence for substantial individual differences in visual cortex connectivity, this indicates there may be a consistent role for the visual cortex in blindness, which may differ for each individual. Further, it suggests that the variability in visual reorganization in blindness across individuals could be used to seek stable neuromarkers for sight rehabilitation and assistive approaches.

Effects of stochastic coding on olfactory discrimination in flies and mice

Sparse coding can improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's benefits: similar sensory stimuli have significant overlap and responses vary across trials. To elucidate the effects of these 2 factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination-the mushroom body (MB) and the piriform cortex (PCx). We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the observed variability arises from noise within central circuits rather than sensory noise. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though these benefits only accrue with extended training with more trials. Overall, we have uncovered a conserved, stochastic coding scheme in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all (WTA) inhibitory circuit, that improves discrimination with training.

Increased neural variability in adolescents with ADHD symptomatology: Evidence from a single-trial EEG study

Increased intrasubject variability of reaction time (RT) refers to inconsistency in an individual's speed of responding to a task. This increased variability has been suggested as a fundamental feature of attention deficit hyperactivity disorder (ADHD), however, its neural sources are still unclear. In this study, we aimed to examine whether such inconsistency at the behavioral level would be accompanied by inconsistency at the neural level; and whether different types of neural and behavioral variability would be related to ADHD symptomatology. We recorded electroencephalogram (EEG) data from 62 adolescents, who were part of a prospective longitudinal study on the development of ADHD. We examined trial-by-trial neural variability in response to visual stimuli in two cognitive tasks. Adolescents with high ADHD symptomatology exhibited an increased neural variability before the presentation of the stimulus, but when presented with a visual stimulus, this variability decreased to a level that was similar to that exhibited by participants with low ADHD symptomatology. In contrast with our prediction, neural variability was unrelated to the magnitude of behavioral variability. Our findings suggest that adolescents with higher symptoms are characterized by increased neural variability before the stimulation, which might reflect a difficulty in alertness to the forthcoming stimulus; but this increased neural variability does not seem to account for their RT variability.

Modulation of cortical beta oscillations influences motor vigor: A rhythmic TMS-EEG study

Previous electro- or magnetoencephalography (Electro/Magneto EncephaloGraphic; E/MEG) studies using a correlative approach have shown that β (13-30 Hz) oscillations emerging in the primary motor cortex (M1) are implicated in regulating motor response vigor and associated with an anti-kinetic role, that is, slowness of movement. However, the functional role of M1 β oscillations in regulation of motor responses remains unclear. To address this gap, we combined EEG with rhythmic TMS (rhTMS) delivered to M1 at the β (20 Hz) frequency shortly before subjects performed an isometric ramp-and-hold finger force production task at three force levels. rhTMS is a novel approach that can modulate rhythmic patterns of neural activity. β-rhTMS over M1 induced a modulation of neural oscillations to β frequency in the sensorimotor area and reduced peak force rate during the ramp-up period relative to sham and catch trials. Interestingly, this rhTMS effect occurred only in the large force production condition. To distinguish whether the effects of rhTMS on EEG and behavior stemmed from phase-resetting by each magnetic pulse or neural entrainment by the periodicity of rhTMS, we performed a control experiment using arrhythmic TMS (arTMS). arTMS did not induce changes in EEG oscillations nor peak force rate during the rump-up period. Our results provide novel evidence that β neural oscillations emerging the sensorimotor area influence the regulation of motor response vigor. Furthermore, our findings further demonstrate that rhTMS is a promising tool for tuning neural oscillations to the target frequency.

Altered EEG variability on different time scales in participants with autism spectrum disorder: an exploratory study

One of the great challenges in psychiatry is finding reliable biomarkers that may allow for more accurate diagnosis and treatment of patients. Neural variability received increasing attention in recent years as a potential biomarker. In the present explorative study we investigated temporal variability in visually evoked EEG activity in a cohort of 16 adult participants with Asperger Syndrome (AS) and 19 neurotypical (NT) controls. Participants performed a visual oddball task using fine and coarse checkerboard stimuli. We investigated various measures of neural variability and found effects on multiple time scales. (1) As opposed to the previous studies, we found reduced inter-trial variability in the AS group compared to NT. (2) This effect builds up over the entire course of a 5-min experiment and (3) seems to be based on smaller variability of neural background activity in AS compared to NTs. The here reported variability effects come with considerably large effect sizes, making them promising candidates for potentially reliable biomarkers in psychiatric diagnostics. The observed pattern of universality across different time scales and stimulation conditions indicates trait-like effects. Further research with a new and larger set of participants are thus needed to verify or falsify our findings.

EEG variability: Task-driven or subject-driven signal of interest?

Neurons in the brain are seldom perfectly quiet. They continually receive input and generate output, resulting in highly variable patterns of ongoing activity. Yet the functional significance of this variability is not well understood. If brain signal variability is functionally relevant and serves as an important indicator of cognitive function, then it should be highly sensitive to the precise manner in which a cognitive system is engaged and/or relate strongly to differences in behavioral performance. To test this, we examined EEG activity in younger adults as they performed a cognitive skill learning task and during rest. Several measures of EEG variability and signal strength were calculated in overlapping time windows that spanned the trial interval. We performed a systematic examination of the factors that most strongly influenced the variability and strength of EEG activity. First, we examined the relative sensitivity of each measure to across-subject variation (within blocks) and across-block variation (within subjects). We found that the across-subject variation in EEG variability and signal strength was much stronger than the across-block variation. Second, we examined the sensitivity of each measure to different sources of across-block variation during skill acquisition. We found that key task-driven changes in EEG activity were best reflected in changes in the strength, rather than the variability, of EEG activity. Lastly, we examined across-subject variation in each measure and its relationship with behavior. We found that individual differences in response time measures were best reflected in individual differences in the variability, rather than the strength, of EEG activity. Importantly, we found that individual differences in EEG variability related strongly to stable indicators of subject identity rather than dynamic indicators of subject performance. We therefore suggest that EEG variability may provide a more sensitive subject-driven measure of individual differences than task-driven signal of interest.

From Shorter to Longer Timescales: Converging Integrated Information Theory (IIT) with the Temporo-Spatial Theory of Consciousness (TTC)

Time is a key element of consciousness as it includes multiple timescales from shorter to longer ones. This is reflected in our experience of various short-term phenomenal contents at discrete points in time as part of an ongoing, more continuous, and long-term ‘stream of consciousness.’ Can Integrated Information Theory (IIT) account for this multitude of timescales of consciousness? According to the theory, the relevant spatiotemporal scale for consciousness is the one in which the system reaches the maximum cause-effect power; IIT currently predicts that experience occurs on the order of short timescales, namely, between 100 and 300 ms (theta and alpha frequency range). This can well account for the integration of single inputs into a particular phenomenal content. However, such short timescales leave open the temporal relation of specific phenomenal contents to others during the course of the ongoing time, that is, the stream of consciousness. For that purpose, we converge the IIT with the Temporo-spatial Theory of Consciousness (TTC), which, assuming a multitude of different timescales, can take into view the temporal integration of specific phenomenal contents with other phenomenal contents over time. On the neuronal side, this is detailed by considering those neuronal mechanisms driving the non-additive interaction of pre-stimulus activity with the input resulting in stimulus-related activity. Due to their non-additive interaction, the single input is not only integrated with others in the short-term timescales of 100–300 ms (alpha and theta frequencies) (as predicted by IIT) but, at the same time, also virtually expanded in its temporal (and spatial) features; this is related to the longer timescales (delta and slower frequencies) that are carried over from pre-stimulus to stimulus-related activity. Such a non-additive pre-stimulus-input interaction amounts to temporo-spatial expansion as a key mechanism of TTC for the constitution of phenomenal contents including their embedding or nesting within the ongoing temporal dynamic, i.e., the stream of consciousness. In conclusion, we propose converging the short-term integration of inputs postulated in IIT (100–300 ms as in the alpha and theta frequency range) with the longer timescales (in delta and slower frequencies) of temporo-spatial expansion in TTC.

Temporo-spatial Theory of Consciousness (TTC) - Bridging the gap of neuronal activity and phenomenal states

Consciousness and its neural mechanisms remain a mystery. Current neuroscientific theories focus predominantly on the external input/stimulus and the associated stimulus-related activity during conscious contents. Despite all progress, we encounter two gaps: (i) a gap between spontaneous and stimulus-related activity; (ii) a gap between neuronal and phenomenal features. A novel, different, and unique approach, Temporo-spatial theory of consciousness (TTC) aims to bridge both gaps. The TTC focuses on the brain's spontaneous activity and how its spatial topography and temporal dynamic shape stimulus-related activity and resurface in the corresponding spatial and temporal features of consciousness, i.e., 'common currency'. The TTC introduces four temporo-spatial mechanisms: expansion, globalization, alignment, and nestedness. These are associated with distinct dimensions of consciousness including phenomenal content, access, form/structure, and level/state, respectively. Following up on the first introduction of the TTC in 2017, we review updates, further develop these temporo-spatial mechanisms, and postulate specific neurophenomenal hypotheses. We conclude that the TTC offers a viable approach for (i) linking spontaneous and stimulus-related activity in conscious states; (ii) determining specific neuronal and neurophenomenal mechanisms for the distinct dimensions of consciousness; (iii) an integrative and unifying framework of different neuroscientific theories of consciousness; and (iv) offers novel empirically grounded conceptual assumptions about the biological and ontological nature of consciousness and its relation to the brain.

Reduced alpha amplitudes predict perceptual suppression

The amplitude of prestimulus alpha oscillations over parieto-occipital cortex has been shown to predict visual detection of masked and threshold-level stimuli. Whether alpha activity similarly predicts target visibility in perceptual suppression paradigms, another type of illusion commonly used to investigate visual awareness, is presently unclear. Here, we examined prestimulus alpha activity in the electroencephalogram (EEG) of healthy participants in the context of a generalized flash suppression (GFS) task during which salient target stimuli are rendered subjectively invisible in a subset of trials following the onset of a full-field motion stimulus. Unlike for masking or threshold paradigms, alpha (8-12 Hz) amplitude prior to motion onset was significantly higher when targets remained subjectively visible compared to trials during which the targets became perceptually suppressed. Furthermore, individual prestimulus alpha amplitudes strongly correlated with the individual trial-to-trial variability quenching following motion stimulus onset, indicating that variability quenching in visual cortex is closely linked to prestimulus alpha activity. We conclude that predictive correlates of conscious perception derived from perceptual suppression paradigms differ substantially from those obtained with "near threshold paradigms", possibly reflecting the effectiveness of the suppressor stimulus.

Prestimulus dynamics blend with the stimulus in neural variability quenching

Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.

Behavior needs neural variability

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

Individual magnitudes of neural variability quenching are associated with motion perception abilities

Remarkable trial-by-trial variability is apparent in cortical responses to repeating stimulus presentations. This neural variability across trials is relatively high before stimulus presentation and then reduced (i.e., quenched) ∼0.2 s after stimulus presentation. Individual subjects exhibit different magnitudes of variability quenching, and previous work from our lab has revealed that individuals with larger variability quenching exhibit lower (i.e., better) perceptual thresholds in a contrast discrimination task. Here, we examined whether similar findings were also apparent in a motion detection task, which is processed by distinct neural populations in the visual system. We recorded EEG data from 35 adult subjects as they detected the direction of coherent motion in random dot kinematograms. The results demonstrated that individual magnitudes of variability quenching were significantly correlated with coherent motion thresholds, particularly when presenting stimuli with low dot densities, where coherent motion was more difficult to detect. These findings provide consistent support for the hypothesis that larger magnitudes of neural variability quenching are associated with better perceptual abilities in multiple visual domain tasks.NEW & NOTEWORTHY The current study demonstrates that better visual perception abilities in a motion discrimination task are associated with larger quenching of neural variability. In line with previous studies and signal detection theory principles, these findings support the hypothesis that cortical sensory neurons increase reproducibility to enhance detection and discrimination of sensory stimuli.

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.

Intraindividual variability in neural activity in the prefrontal cortex during active walking in older adults

Intraindividual variability in gait and cognitive performance is distinct from central-tendency measures and associated with clinical outcomes in aging. Knowledge concerning intraindividual variability in neural activity, however, has been relatively scarce, and no research to date has reported on such variability during active walking. The current study addressed this major gap in knowledge. Participants were community-residing older adults (n = 394; mean age = 76.29 ± 6.65 years; %female = 55). Functional near-infrared spectroscopy (fNIRS) was used to measure oxygenated hemoglobin (HbO2) in the prefrontal cortex under three experimental conditions: single-task-walk, single-task-alpha (cognitive task), and dual-task-walk, which required the participants to perform the two single tasks simultaneously. Intraindividual variability in neural activity was operationalized using the standard deviation of fNIRS-derived HbO2 observations assessed during a 30-s interval in each experimental condition. The increase in intraindividual variability in neural activity in the dual-task-walk condition compared to both single-task conditions was associated with the presence of cognitive impairments and being a male. Furthermore, measures of intraindividual variability in neural activity and gait performance were positively correlated only under the dual-task-walk condition. Intraindividual variability in the neural activity of gait may be a novel marker for age-related impairments in mobility and cognitive function. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Characterizing the brain's dynamical response from scalp-level neural electrical signals: a review of methodology development

The brain displays dynamical system behaviors at various levels that are functionally and cognitively relevant. Ample researches have examined how the dynamical properties of brain activity reflect the neural cognitive working mechanisms. A prevalent approach in this field is to extract the trial-averaged brain electrophysiological signals as a representation of the dynamical response of the complex neural system to external stimuli. However, the responses are intrinsically variable in latency from trial to trial. The variability compromises the accuracy of the detected dynamical response pattern based on trial-averaged approach, which may mislead subsequent modelling works. More accurate characterization of the brain's dynamical response incorporating single trial variability information is of profound significance in deepening our understanding of neural cognitive dynamics and brain's working principles. Various methods have been attempted to address the trial-to-trial asynchrony issue in order to achieve an improved representation of the dynamical response. We review the latest development of methodology in this area and the contribution of latency variability-based decomposition and reconstruction of dynamical response to neural cognitive researches.

Age-related variability in decision-making: Insights from neurochemistry

Despite dopamine's significant role in models of value-based decision-making and findings demonstrating loss of dopamine function in aging, evidence of systematic changes in decision-making over the life span remains elusive. Previous studies attempting to resolve the neural basis of age-related alteration in decision-making have typically focused on physical age, which can be a poor proxy for age-related effects on neural systems. There is growing appreciation that aging has heterogeneous effects on distinct components of the dopamine system within subject in addition to substantial variability between subjects. We propose that some of the conflicting findings in age-related effects on decision-making may be reconciled if we can observe the underlying dopamine components within individuals. This can be achieved by incorporating in vivo imaging techniques including positron emission tomography (PET) and neuromelanin-sensitive MR. Further, we discuss how affective factors may contribute to individual differences in decision-making performance among older adults. Specifically, we propose that age-related shifts in affective attention ("positivity effect") can, in some cases, counteract the impact of altered dopamine function on specific decision-making processes, contributing to variability in findings. In an effort to provide clarity to the field and advance productive hypothesis testing, we propose ways in which in vivo dopamine imaging can be leveraged to disambiguate dopaminergic influences on decision-making, and suggest strategies for assessing individual differences in the contribution of affective attentional focus.

The Relationship between Trial-by-Trial Variability and Oscillations of Cortical Population Activity

Neural activity fluctuates over time, creating considerable variability across trials. This trial-by-trial neural variability is dramatically reduced (“quenched”) after the presentation of sensory stimuli. Likewise, the power of neural oscillations, primarily in the alpha-beta band, is also reduced after stimulus onset. Despite their similarity, these phenomena have so far been studied and discussed independently. We hypothesized that the two phenomena are tightly coupled in electrophysiological recordings of large cortical neural populations. To test this, we examined magnetoencephalography (MEG) recordings of healthy subjects viewing repeated presentations of a visual stimulus. The timing, amplitude, and spatial topography of variability-quenching and power-suppression were remarkably similar. Neural variability quenching was eliminated by excluding the alpha-beta band from the recordings, but not by excluding other frequency-bands. Moreover, individual magnitudes of alpha-beta band-power explained 86% of between-subject differences in variability quenching. An alternative mechanism that may generate variability quenching is increased phase alignment across trials. However, changes in inter-trial-phase-coherence (ITPC) exhibited distinct timing and no correlations with the magnitude of variability quenching in individual participants. These results reveal that neural variability quenching is tightly coupled with stimulus-induced changes in the power of alpha-beta band oscillations, associating two phenomena that have so far been studied in isolation.

Atypical Temporal Dynamics of Resting State Shapes Stimulus-Evoked Activity in Depression-An EEG Study on Rest-Stimulus Interaction

Major depressive disorder (MDD) is a complex psychiatric disorder characterized by changes in both resting state and stimulus-evoked activity. Whether resting state changes are carried over to stimulus-evoked activity, however, is unclear. We conducted a combined rest (3 min) and task (three-stimulus auditory oddball paradigm) EEG study in n=28 acute depressed MDD patients, comparing them with n=25 healthy participants. Our focus was on the temporal dynamics of both resting state and stimulus-evoked activity for which reason we measured peak frequency (PF), coefficient of variation (CV), Lempel-Ziv complexity (LZC), and trial-to-trial variability (TTV). Our main findings are: i) atypical temporal dynamics in resting state, specifically in the alpha and theta bands as measured by peak frequency (PF), coefficient of variation (CV) and power; ii) decreased reactivity to external deviant stimuli as measured by decreased changes in stimulus-evoked variance and complexity-TTV, LZC, and power and frequency sliding (FS and PS); iii) correlation of stimulus related measures (TTV, LZC, PS, and FS) with resting state measures. Together, our findings show that resting state dynamics alone are atypical in MDD and, even more important, strongly shapes the dynamics of subsequent stimulus-evoked activity. We thus conclude that MDD can be characterized by an atypical temporal dynamic of its rest-stimulus interaction; that, in turn, makes it difficult for depressed patients to react to relevant stimuli such as the deviant tone in our paradigm.

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.

Heterogeneity of the Anxiety-Related Attention Bias: A Review and Working Model for Future Research

The anxiety-related attention bias (AB) has been studied for several decades as a clinically-relevant output of the dynamic and complex threat detection-response system. Despite research enthusiasm for the construct of AB, current theories and measurement approaches cannot adequately account for the growing body of mixed, contradictory, and null findings. Drawing on clinical, neuroscience, and animal models, we argue that the apparent complexity and contradictions in the empirical literature can be attributed to the field's failure to clearly conceptualize AB heterogeneity and the dearth of studies in AB that consider additional cognitive mechanisms in anxiety, particularly disruptions in threat-safety discrimination and cognitive control. We review existing research and propose a working model of AB heterogeneity positing that AB may be best conceptualized as multiple subtypes of dysregulated processing of and attention to threat anchored in individual differences in threat-safety discrimination and cognitive control. We review evidence for this working model and discuss how it can be used to advance knowledge of AB mechanisms and inform personalized prevention and intervention approaches.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.

Disrupted neural variability during propofol-induced sedation and unconsciousness

Variability quenching is a widespread neural phenomenon in which trial-to-trial variability (TTV) of neural activity is reduced by repeated presentations of a sensory stimulus. However, its neural mechanism and functional significance remain poorly understood. Recurrent network dynamics are suggested as a candidate mechanism of TTV, and they play a key role in consciousness. We thus asked whether the variability-quenching phenomenon is related to the level of consciousness. We hypothesized that TTV reduction would be compromised during reduced level of consciousness by propofol anesthetics. We recorded functional magnetic resonance imaging signals of resting-state and stimulus-induced activities in three conditions: wakefulness, sedation, and unconsciousness (i.e., deep anesthesia). We measured the average (trial-to-trial mean, TTM) and variability (TTV) of auditory stimulus-induced activity under the three conditions. We also examined another form of neural variability (temporal variability, TV), which quantifies the overall dynamic range of ongoing neural activity across time, during both the resting-state and the task. We found that (a) TTM deceased gradually from wakefulness through sedation to anesthesia, (b) stimulus-induced TTV reduction normally seen during wakefulness was abolished during both sedation and anesthesia, and (c) TV increased in the task state as compared to resting-state during both wakefulness and sedation, but not anesthesia. Together, our results reveal distinct effects of propofol on the two forms of neural variability (TTV and TV). They imply that the anesthetic disrupts recurrent network dynamics, thus prevents the stabilization of cortical activity states. These findings shed new light on the temporal dynamics of neuronal variability and its alteration during anesthetic-induced unconsciousness.

The temporal signature of self: Temporal measures of resting-state EEG predict self-consciousness

The self is the core of our mental life. Previous investigations have demonstrated a strong neural overlap between self-related activity and resting state activity. This suggests that information about self-relatedness is encoded in our brain's spontaneous activity. The exact neuronal mechanisms of such "rest-self containment," however, remain unclear. The present EEG study investigated temporal measures of resting state EEG to relate them to self-consciousness. This was obtained with the self-consciousness scale (SCS) which measures Private, Public, and Social dimensions of self. We demonstrate positive correlations between Private self-consciousness and three temporal measures of resting state activity: scale-free activity as indexed by the power-law exponent (PLE), the auto-correlation window (ACW), and modulation index (MI). Specifically, higher PLE, longer ACW, and stronger MI were related to higher degrees of Private self-consciousness. Finally, conducting eLORETA for spatial tomography, we found significant correlation of Private self-consciousness with activity in cortical midline structures such as the perigenual anterior cingulate cortex and posterior cingulate cortex. These results were reinforced with a data-driven analysis; a machine learning algorithm accurately predicted an individual as having a "high" or "low" Private self-consciousness score based on these measures of the brain's spatiotemporal structure. In conclusion, our results demonstrate that Private self-consciousness is related to the temporal structure of resting state activity as featured by temporal nestedness (PLE), temporal continuity (ACW), and temporal integration (MI). Our results support the hypothesis that self-related information is temporally contained in the brain's resting state. "Rest-self containment" can thus be featured by a temporal signature.

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.