Stimulus-induced change in long-range temporal correlations and scaling behaviour of sensorimotor oscillations

Clinical and neuropsychological correlates of theta-band functional excitation-inhibition ratio in autism: An EEG study

OBJECTIVE

How abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD symptomatology.

METHODS

A total of 83 ASD and typically developing (TD) individuals participated in this study. Participants' interference control and set-shifting abilities were assessed. Resting-state electroencephalography (EEG) was used for estimating theta-band fE/I ratio.

RESULTS

ASD individuals (n = 31 without visual EEG abnormality; n = 22 with visual EEG abnormality) generally performed slower in a cognitive task tapping interference control and set-maintenance abilities, but only ASD individuals with visually abnormal EEG performed significantly slower than their TD counterparts (Bonferroni-corrected ps < .001). Heightened theta-band fE/I ratios at the whole-head level, left and right hemispheres were observed in the ASD subgroup without visual EEG abnormality only (Bonferroni-corrected ps < .001), which remained highly significant when only data from medication-naïve participants were analyzed. In addition, higher left hemispheric fE/I ratios in ASD individuals without visual EEG abnormality were significantly correlated with faster interference control task performance, in turn faster reaction time was significantly associated with less severe restricted, repetitive behavior (Bonferroni-corrected ps ≤ .0017).

CONCLUSIONS

Differential theta-band fE/I within the ASD population. Heightened theta-band fE/I in ASD without visual EEG abnormality may be associated with more efficient filtering of distractors and a less severe ASD symptom manifestation.

SIGNIFICANCE

Brain signaling, indicated by theta-band fE/I, was different in ASD subgroups. Only ASD with visually-normal EEG showed heightened theta-band fE/I, which was associated with faster processing of visual distractors during a cognitive task. More efficient distractor filtering was associated with less restricted, repetitive behaviors.

Multi-scale entropy assessment of magnetoencephalography signals in schizophrenia

Schizophrenia is a severe disruption in cognition and emotion, affecting fundamental human functions. In this study, we applied Multi-Scale Entropy analysis to resting-state Magnetoencephalography data from 54 schizophrenia patients and 98 healthy controls. This method quantifies the temporal complexity of the signal across different time scales using the concept of sample entropy. Results show significantly higher sample entropy in schizophrenia patients, primarily in central, parietal, and occipital lobes, peaking at time scales equivalent to frequencies between 15 and 24 Hz. To disentangle the contributions of the amplitude and phase components, we applied the same analysis to a phase-shuffled surrogate signal. The analysis revealed that most differences originate from the amplitude component in the δ, α, and β power bands. While the phase component had a smaller magnitude, closer examination reveals clear spatial patterns and significant differences across specific brain regions. We assessed the potential of multi-scale entropy as a schizophrenia biomarker by comparing its classification performance to conventional spectral analysis and a cognitive task (the n-back paradigm). The discriminative power of multi-scale entropy and spectral features was similar, with a slight advantage for multi-scale entropy features. The results of the n-back test were slightly below those obtained from multi-scale entropy and spectral features.

Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus

Objective. The development of electrical pulse stimulations in brain, including deep brain stimulation, is promising for treating various brain diseases. However, the mechanisms of brain stimulations are not yet fully understood. Previous studies have shown that the commonly used high-frequency stimulation (HFS) can increase the firing of neurons and modulate the pattern of neuronal firing. Because the generation of neuronal firing in brain is a nonlinear process, investigating the characteristics of nonlinear dynamics induced by HFS could be helpful to reveal more mechanisms of brain stimulations. The aim of present study is to investigate the fractal properties in the neuronal firing generated by HFS.Approach. HFS pulse sequences with a constant frequency 100 Hz were applied in the afferent fiber tracts of rat hippocampal CA1 region. Unit spikes of both the pyramidal cells and the interneurons in the downstream area of stimulations were recorded. Two fractal indexes-the Fano factor and Hurst exponent were calculated to evaluate the changes of long-range temporal correlations (LRTCs), a typical characteristic of fractal process, in spike sequences of neuronal firing.Mainresults. Neuronal firing at both baseline and during HFS exhibited LRTCs over multiple time scales. In addition, the LRTCs significantly increased during HFS, which was confirmed by simulation data of both randomly shuffled sequences and surrogate sequences.Conclusion. The purely periodic stimulation of HFS pulses, a non-fractal process without LRTCs, can increase rather than decrease the LRTCs in neuronal firing.Significance. The finding provides new nonlinear mechanisms of brain stimulation and suggests that LRTCs could be a new biomarker to evaluate the nonlinear effects of HFS.

Decreased long-range temporal correlations in the resting-state functional magnetic resonance imaging blood-oxygen-level-dependent signal reflect motor sequence learning up to 2 weeks following training

Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity.

Neuromodulatory control of complex adaptive dynamics in the brain

How is the massive dimensionality and complexity of the microscopic constituents of the nervous system brought under sufficiently tight control so as to coordinate adaptive behaviour? A powerful means for striking this balance is to poise neurons close to the critical point of a phase transition, at which a small change in neuronal excitability can manifest a nonlinear augmentation in neuronal activity. How the brain could mediate this critical transition is a key open question in neuroscience. Here, I propose that the different arms of the ascending arousal system provide the brain with a diverse set of heterogeneous control parameters that can be used to modulate the excitability and receptivity of target neurons-in other words, to act as control parameters for mediating critical neuronal order. Through a series of worked examples, I demonstrate how the neuromodulatory arousal system can interact with the inherent topological complexity of neuronal subsystems in the brain to mediate complex adaptive behaviour.

The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder

We study how obsessive-compulsive disorder (OCD) affects the complexity and time-reversal symmetry-breaking (irreversibility) of the brain resting-state activity as measured by magnetoencephalography (MEG). Comparing MEG recordings from OCD patients and age/sex matched control subjects, we find that irreversibility is more concentrated at faster time scales and more uniformly distributed across different channels of the same hemisphere in OCD patients than in control subjects. Furthermore, the interhemispheric asymmetry between homologous areas of OCD patients and controls is also markedly different. Some of these differences were reduced by 1-year of Kundalini Yoga meditation treatment. Taken together, these results suggest that OCD alters the dynamic attractor of the brain's resting state and hint at a possible novel neurophysiological characterization of this psychiatric disorder and how this therapy can possibly modulate brain function.

Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena

Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.

Attaining the recesses of the cognitive space

Existing neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury.

Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks

Brain activity is composed of oscillatory and broadband arrhythmic components; however, there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal dynamics of broadband arrhythmic electroencephalography (EEG) remain unexplored. We have previously demonstrated that broadband arrhythmic EEG contains both short- and long-range temporal correlations that change significantly during movement. In this study, we build upon our previous work to gain a deeper understanding of these changes in the long-range temporal correlation (LRTC) in broadband EEG and contrast them with the well-known LRTC in alpha oscillation amplitude typically found in the literature. We investigate and validate changes in LRTCs during five different types of movements and motor imagery tasks using two independent EEG datasets recorded with two different paradigms-our finger tapping dataset with single self-initiated asynchronous finger taps and publicly available EEG dataset containing cued continuous movement and motor imagery of fists and feet. We quantified instantaneous changes in broadband LRTCs by detrended fluctuation analysis on single trial 2 s EEG sliding windows. The broadband LRTC increased significantly (p < 0.05) during all motor tasks as compared to the resting state. In contrast, the alpha oscillation LRTC, which had to be computed on longer stitched EEG segments, decreased significantly (p < 0.05) consistently with the literature. This suggests the complementarity of underlying fast and slow neuronal scale-free dynamics during movement and motor imagery. The single trial broadband LRTC gave high average binary classification accuracy in the range of 70.54±10.03% to 76.07±6.40% for all motor execution and imagery tasks and hence can be used in brain-computer interface (BCI). Thus, we demonstrate generalizability, robustness, and reproducibility of novel motor neural correlate, the single trial broadband LRTC, during different motor execution and imagery tasks in single asynchronous and cued continuous motor-BCI paradigms and its contrasting behavior with LRTC in alpha oscillation amplitude.

Differential classification of states of consciousness using envelope- and phase-based functional connectivity

The development of sophisticated computational tools to quantify changes in the brain's oscillatory dynamics across states of consciousness have included both envelope- and phase-based measures of functional connectivity (FC), but there are very few direct comparisons of these techniques using the same dataset. The goal of this study was to compare an envelope-based (i.e. Amplitude Envelope Correlation, AEC) and a phase-based (i.e. weighted Phase Lag Index, wPLI) measure of FC in their classification of states of consciousness. Nine healthy participants underwent a three-hour experimental anesthetic protocol with propofol induction and isoflurane maintenance, in which five minutes of 128-channel electroencephalography were recorded before, during, and after anesthetic-induced unconsciousness, at the following time points: Baseline; light sedation with propofol (Light Sedation); deep unconsciousness following three hours of surgical levels of anesthesia with isoflurane (Unconscious); five minutes prior to the recovery of consciousness (Pre-ROC); and three hours following the recovery of consciousness (Recovery). Support vector machine classification was applied to the source-localized EEG in the alpha (8-13 Hz) frequency band in order to investigate the ability of AEC and wPLI (separately and together) to discriminate i) the four states from Baseline; ii) Unconscious ("deep" unconsciousness) vs. Pre-ROC ("light" unconsciousness); and iii) responsiveness (Baseline, Light Sedation, Recovery) vs. unresponsiveness (Unconscious, Pre-ROC). AEC and wPLI yielded different patterns of global connectivity across states of consciousness, with AEC showing the strongest network connectivity during the Unconscious epoch, and wPLI showing the strongest connectivity during full consciousness (i.e., Baseline and Recovery). Both measures also demonstrated differential predictive contributions across participants and used different brain regions for classification. AEC showed higher classification accuracy overall, particularly for distinguishing anesthetic-induced unconsciousness from Baseline (83.7 ± 0.8%). AEC also showed stronger classification accuracy than wPLI when distinguishing Unconscious from Pre-ROC (i.e., "deep" from "light" unconsciousness) (AEC: 66.3 ± 1.2%; wPLI: 56.2 ± 1.3%), and when distinguishing between responsiveness and unresponsiveness (AEC: 76.0 ± 1.3%; wPLI: 63.6 ± 1.8%). Classification accuracy was not improved compared to AEC when both AEC and wPLI were combined. This analysis of source-localized EEG data demonstrates that envelope- and phase-based FC provide different information about states of consciousness but that, on a group level, AEC is better able to detect relative alterations in brain FC across levels of anesthetic-induced unconsciousness compared to wPLI.

Detrended fluctuation analysis in the presurgical evaluation of parietal lobe epilepsy patients

OBJECTIVE

To examine the usability of long-range temporal correlations (LRTCs) in non-invasive localization of the epileptogenic zone (EZ) in refractory parietal lobe epilepsy (RPLE) patients.

METHODS

We analyzed 10 RPLE patients who had presurgical MEG and underwent epilepsy surgery. We quantified LRTCs with detrended fluctuation analysis (DFA) at four frequency bands for 200 cortical regions estimated using individual source models. We correlated individually the DFA maps to the distance from the resection area and from cortical locations of interictal epileptiform discharges (IEDs). Additionally, three clinical experts inspected the DFA maps to visually assess the most likely EZ locations.

RESULTS

The DFA maps correlated with the distance to resection area in patients with type II focal cortical dysplasia (FCD) (p<0.05), but not in other etiologies. Similarly, the DFA maps correlated with the IED locations only in the FCD II patients. Visual analysis of the DFA maps showed high interobserver agreement and accuracy in FCD patients in assigning the affected hemisphere and lobe.

CONCLUSIONS

Aberrant LRTCs correlate with the resection areas and IED locations.

SIGNIFICANCE

This methodological pilot study demonstrates the feasibility of approximating cortical LRTCs from MEG that may aid in the EZ localization and provide new non-invasive insight into the presurgical evaluation of epilepsy.

Revisiting Functional Connectivity for Infraslow Scale-Free Brain Dynamics Using Complex Wavelets

The analysis of human brain functional networks is achieved by computing functional connectivity indices reflecting phase coupling and interactions between remote brain regions. In magneto- and electroencephalography, the most frequently used functional connectivity indices are constructed based on Fourier-based cross-spectral estimation applied to specific fast and band-limited oscillatory regimes. Recently, infraslow arrhythmic fluctuations (below the 1 Hz) were recognized as playing a leading role in spontaneous brain activity. The present work aims to propose to assess functional connectivity from fractal dynamics, thus extending the assessment of functional connectivity to the infraslow arrhythmic or scale-free temporal dynamics of M/EEG-quantified brain activity. Instead of being based on Fourier analysis, new Imaginary Coherence and weighted Phase Lag indices are constructed from complex-wavelet representations. Their performances are first assessed on synthetic data by means of Monte-Carlo simulations, and they are then compared favorably against the classical Fourier-based indices. These new assessments of functional connectivity indices are also applied to MEG data collected on 36 individuals both at rest and during the learning of a visual motion discrimination task. They demonstrate a higher statistical sensitivity, compared to their Fourier counterparts, in capturing significant and relevant functional interactions in the infraslow regime and modulations from rest to task. Notably, the consistent overall increase in functional connectivity assessed from fractal dynamics from rest to task correlated with a change in temporal dynamics as well as with improved performance in task completion, which suggests that the complex-wavelet weighted Phase Lag index is the sole index is able to capture brain plasticity in the infraslow scale-free regime.

Individual differences in anticipatory mu rhythm modulation are associated with executive function and processing speed

There is increasing interest in the role of brain oscillations in the regulation and control of behavior. The current study examined the relations between specific cognitive abilities and changes in brain oscillatory activity during anticipation of, and in response to, tactile stimulation of the hand. The oscillation of interest was the sensorimotor mu rhythm (8-14 Hz) at central electrode sites. The electroencephalogram (EEG) was recorded during a task in which a visuospatial cue directed adults (N = 40) that a tactile stimulus would be delivered to their left or right hand. Lateralized changes in mu power following tactile stimulation were associated with reaction time to the tactile stimulus. The extent of a contralateral anticipatory reduction in mu power during the 500 ms before the tactile stimulus was associated with performance on a separate processing speed task. Changes in ipsilateral mu power during anticipation of the tactile stimulus were associated with performance on a flanker task and were marginally correlated with performance on a card sort task. Regression analyses further indicated the specificity of these relations to anticipatory changes in mu power. In summary, mu rhythm modulation during anticipation of tactile stimulation to a specific bodily location was related to a broad measure of processing speed and to variability in the broader ability to regulate behavior in a goal-directed manner. Implications are discussed in terms of the foundational role of anticipatory attention in cognitive processes and the utility of selective attention to the body as an index of attentional control more broadly.

The Analysis of Electroencephalography Changes Before and After a Single Neurofeedback Alpha/Theta Training Session in University Students

The underlying mechanisms of alpha/theta neurofeedback training have not been fully determined. Therefore, this study aimed to test the changes in the brain state feedback during the alpha/theta training. Twenty-seven healthy participants were trained during a single session of the alpha/theta protocol, and the resting quantitative electroencephalography (QEEG) was assessed before and after training. QEEG was recorded at eight scalp locations (F3, F4, C3, C4, T3, T4, O1, and O2), and the absolute power, relative power, ratio of sensory-motor rhythm beta (SMR) to theta (RST), ratio of SMR-mid beta to theta (RSMT), ratio of mid beta to theta (RMT), ratio of alpha to high beta (RAHB), and scaling exponent of detrended fluctuation analysis by each band were measured. The results indicated a significant increase of absolute alpha power, especially the slow alpha band, at all electrodes except T3 and T4. Moreover, the relative alpha power, especially the slow alpha band, showed a significant increase at all electrodes. The relative theta power showed a significant decrease at all electrodes, except T3. A significant decrease in relative beta power, relative lower beta power and relative mid beta power was observed at O1. RST (at C4, O1, and O2), RSMT and RMT (at F4, C4, O1 and O2), and RAHB (at all electrodes) showed significant increase. Scaling exponents at all electrodes except T3 showed a significant decrease. These findings indicate that a one-time session of alpha/theta training might have the possibility to enhance both vigilance and concentration, thus stabilizing the overall brain function.

The dynamics of resting-state alpha oscillations predict individual differences in creativity

The neuronal mechanisms underlying creativity are poorly understood. Arguably, the brain's ability to switch states would contribute to achieving novel ideas, and thus to creativity. Faster brain-state switching is reflected in the temporal dynamics of functional brain activity. Stronger autocorrelations in brain activity measures can make a brain stay in a certain state for longer periods, whereas low temporal autocorrelations reflect faster state switching. We established the brain's inherent tendency to switch or stay in a resting, no-task condition using 128 channel electroencephalography (EEG). We assessed temporal autocorrelations of the amplitude modulation of the dominant alpha oscillations (8-13 Hz). Creativity was measured by a self-rating, an examiner-rating and the alternative uses task in 40 healthy young adults, which was scored on dimensions of verbal fluency, originality, elaboration, usefulness, and flexibility. For each dimension, the total number of subject-reported alternative uses that matched the criterion was noted (the quantity measure), as well as the proportion of uses that matched the dimensional criterion. A principal components analysis confirmed the two-component structure of quantity and quality. Partial correlation analysis was used controlling for gender and age, and a cluster permutation test was performed to correct for multiple testing. A significant cluster over right central/temporal brain areas was found with a negative correlation between creativity and temporal autocorrelations were observed (p = 0.028). To our knowledge, this is the first demonstration that individual variation in the dynamically changing activity in the brain may offer a neuronal explanation for individual variation in creative ideation.

Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex

Preterm infant brain activity is discontinuous; bursts of activity recorded using EEG (electroencephalography), thought to be driven by subcortical regions, display scale free properties and exhibit a complex temporal ordering known as long-range temporal correlations (LRTCs). During brain development, activity-dependent mechanisms are essential for synaptic connectivity formation, and abolishing burst activity in animal models leads to weak disorganised synaptic connectivity. Moreover, synaptic pruning shares similar mechanisms to spike-timing dependent plasticity (STDP), suggesting that the timing of activity may play a critical role in connectivity formation. We investigated, in a computational model of leaky integrate-and-fire neurones, whether the temporal ordering of burst activity within an external driving input could modulate connectivity formation in the network. Connectivity evolved across the course of simulations using an approach analogous to STDP, from networks with initial random connectivity. Small-world connectivity and hub neurones emerged in the network structure—characteristic properties of mature brain networks. Notably, driving the network with an external input which exhibited LRTCs in the temporal ordering of burst activity facilitated the emergence of these network properties, increasing the speed with which they emerged compared with when the network was driven by the same input with the bursts randomly ordered in time. Moreover, the emergence of small-world properties was dependent on the strength of the LRTCs. These results suggest that the temporal ordering of burst activity could play an important role in synaptic connectivity formation and the emergence of small-world topology in the developing brain.

Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement

Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is mostly considered as emerging from oscillations of the neuronal populations. However, the changes in the temporal dynamics, especially in the broadband arrhythmic EEG have not been investigated for movement intention detection. The Long-Range Temporal Correlations (LRTC) are ubiquitously present in several neuronal processes, typically requiring longer timescales to detect. In this paper, we study the ongoing changes in the dynamics of long- as well as short-range temporal dependencies in the single trial broadband EEG during movement intention. We obtained LRTC in 2 s windows of broadband EEG and modeled it using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model which allowed simultaneous modeling of short- and long-range temporal correlations. There were significant (p < 0.05) changes in both broadband long- and short-range temporal correlations during movement intention and execution. We discovered that the broadband LRTC and narrowband ERD are complementary processes providing distinct information about movement because eliminating LRTC from the signal did not affect the ERD and conversely, eliminating ERD from the signal did not affect LRTC. Exploring the possibility of applications in Brain Computer Interfaces (BCI), we used hybrid features with combinations of LRTC, ARFIMA, and ERD to detect movement intention. A significantly higher (p < 0.05) classification accuracy of 88.3 ± 4.2% was obtained using the combination of ARFIMA and ERD features together, which also predicted the earliest movement at 1 s before its onset. The ongoing changes in the long- and short-range temporal correlations in broadband EEG contribute to effectively capturing the motor command generation and can be used to detect movement successfully. These temporal dependencies provide different and additional information about the movement.

Strong long-range temporal correlations of beta/gamma oscillations are associated with poor sustained visual attention performance

Neuronal oscillations exhibit complex amplitude fluctuations with autocorrelations that persist over thousands of oscillatory cycles. Such long-range temporal correlations (LRTC) are thought to reflect neuronal systems poised near a critical state, which would render them capable of quick reorganization and responsive to changing processing demands. When we concentrate, however, the influence of internal and external sources of distraction is better reduced, suggesting that neuronal systems involved with sustained attention could benefit from a shift toward the less volatile sub-critical state. To test these ideas, we recorded electroencephalography (EEG) from healthy volunteers during eyes-closed rest and during a sustained attention task requiring a speeded response to images deviating in their presentation duration. We show that for oscillations recorded during rest, high levels of alpha-band LRTC in the sensorimotor region predicted good reaction-time performance in the attention task. During task execution, however, fast reaction times were associated with high-amplitude beta and gamma oscillations with low LRTC. Finally, we show that reduced LRTC during the attention task compared to the rest condition correlates with better performance, while increased LRTC of oscillations from rest to attention is associated with reduced performance. To our knowledge, this is the first empirical evidence that 'resting-state criticality' of neuronal networks predicts swift behavioral responses in a sensorimotor task, and that steady attentive processing of visual stimuli requires brain dynamics with suppressed temporal complexity.

Attenuation of long-range temporal correlations of neuronal oscillations in young children with autism spectrum disorder

Although autism spectrum disorder (ASD) was previously found to be associated with aberrant brain structure, neuronal amplitudes and spatial neuronal interactions, surprisingly little is known about the temporal dynamics of neuronal oscillations in this disease. Here, the hemoglobin concentration signals (i.e., oxy-Hb and deoxy-Hb) of young children with ASD and typically developing (TD) children were recorded via functional near infrared spectroscopy (fNIRS) when they were watching a cartoon. The long-range temporal correlations (LRTCs) of hemoglobin concentration signals were quantified using detrended fluctuation analysis (DFA). Compared with TD group, the DFA exponents of young children with ASD were significantly smaller over left temporal region for oxy-Hb signal, and over bilateral temporo-occipital regions for deoxy-Hb signals, indicating a shift-to-randomness of brain oscillations in the children with ASD. Testing the relationship between age and DFA exponents revealed that this association could be modulated by autism. The correlation coefficients between age and DFA exponents were significantly more positive in TD group, compared to those in ASD group over several brain regions. Furthermore, the DFA exponents of oxy-Hb in left temporal region were negatively correlated with autistic symptom severity. These results suggest that the decreased DFA exponent of hemoglobin concentration signals may be one of the pathologic changes in ASD, and studying the temporal structure of brain activity via fNIRS technique may provide physiological indicators for autism.

•

The LRTCs of fNIRS signals are attenuated in young children with ASD.

•

Opposite relationships between age and LRTCs of fNIRS signals are revealed in young children with ASD and TD.

•

The LRTCs of oxy-Hb in left temporal region are negatively correlated with autistic symptom severity.

Controlling the Temporal Structure of Brain Oscillations by Focused Attention Meditation

Our focus of attention naturally fluctuates between different sources of information even when we desire to focus on a single object. Focused attention (FA) meditation is associated with greater control over this process, yet the neuronal mechanisms underlying this ability are not entirely understood. Here, we hypothesize that the capacity of attention to transiently focus and swiftly change relates to the critical dynamics emerging when neuronal systems balance at a point of instability between order and disorder. In FA meditation, however, the ability to stay focused is trained, which may be associated with a more homogeneous brain state. To test this hypothesis, we applied analytical tools from criticality theory to EEG in meditation practitioners and meditation-naïve participants from two independent labs. We show that in practitioners-but not in controls-FA meditation strongly suppressed long-range temporal correlations (LRTC) of neuronal oscillations relative to eyes-closed rest with remarkable consistency across frequency bands and scalp locations. The ability to reduce LRTC during meditation increased after one year of additional training and was associated with the subjective experience of fully engaging one's attentional resources, also known as absorption. Sustained practice also affected normal waking brain dynamics as reflected in increased LRTC during an eyes-closed rest state, indicating that brain dynamics are altered beyond the meditative state. Taken together, our findings suggest that the framework of critical brain dynamics is promising for understanding neuronal mechanisms of meditative states and, specifically, we have identified a clear electrophysiological correlate of the FA meditation state.

Catecholamines alter the intrinsic variability of cortical population activity and perception

The ascending modulatory systems of the brain stem are powerful regulators of global brain state. Disturbances of these systems are implicated in several major neuropsychiatric disorders. Yet, how these systems interact with specific neural computations in the cerebral cortex to shape perception, cognition, and behavior remains poorly understood. Here, we probed into the effect of two such systems, the catecholaminergic (dopaminergic and noradrenergic) and cholinergic systems, on an important aspect of cortical computation: its intrinsic variability. To this end, we combined placebo-controlled pharmacological intervention in humans, recordings of cortical population activity using magnetoencephalography (MEG), and psychophysical measurements of the perception of ambiguous visual input. A low-dose catecholaminergic, but not cholinergic, manipulation altered the rate of spontaneous perceptual fluctuations as well as the temporal structure of "scale-free" population activity of large swaths of the visual and parietal cortices. Computational analyses indicate that both effects were consistent with an increase in excitatory relative to inhibitory activity in the cortical areas underlying visual perceptual inference. We propose that catecholamines regulate the variability of perception and cognition through dynamically changing the cortical excitation-inhibition ratio. The combined readout of fluctuations in perception and cortical activity we established here may prove useful as an efficient and easily accessible marker of altered cortical computation in neuropsychiatric disorders.

Attenuation of temporal correlations of neuronal oscillations in patients with mild spastic diplegia

The aim of this study was to investigate the temporal correlations of neuronal oscillations in patients with mild spastic diplegia (MSD). Resting-state electroencephalography (EEG) was recorded from 15 male adolescent and young adult patients with MSD and 15 healthy controls. We characterized the temporal correlations of neuronal oscillations, both on long temporal scale (i.e., >1 second) and short-to-intermediate temporal scale (i.e., <≈1 second) using detrended fluctuation analysis (DFA) and an analysis of the life- and waiting-time statistics of oscillation bursts respectively. The DFA exponents at alpha and beta bands, the life-time biomarker of alpha oscillation, and the life- and waiting-time biomarkers of beta oscillation were significantly attenuated in the patients compared with controls. Moreover, altered scalp distributions of some temporal correlation measures were found at alpha and beta bands in these patients. All these findings suggest that MSD is associated with highly volatile neuronal states of alpha and beta oscillations on short-to-intermediate and much longer time scales, which may be related to cognitive dysfunction in patients with MSD.

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components coordinate to perform specific functions while also exhibiting the potential to re-structure and then perform other functions as task demands change. Consistent with this notion, within cognitive neuroscience it is increasingly accepted that the brain flexibly coordinates the networks needed to cope with changing task demands. However, evaluation of various indices of soft-assembly has so far been absent from neurophysiological research. To begin addressing this gap, we investigated task-related changes in two distinct indices of soft-assembly using the established phenomenon of EEG repetition suppression. In a repetition priming task, we assessed evidence for changes in the correlation dimension and fractal scaling exponents during stimulus-locked event-related potentials, as a function of stimulus onset and familiarity, and relative to spontaneous non-task-related activity. Consistent with predictions derived from soft-assembly, results indicated decreases in dimensionality and increases in fractal scaling exponents from resting to pre-stimulus states and following stimulus onset. However, contrary to predictions, familiarity tended to increase dimensionality estimates. Overall, the findings support the view from soft-assembly that neural dynamics should become increasingly ordered as external task demands increase, and support the broader application of soft-assembly logic in understanding human behavior and electrophysiology.

Alterations in the brain's connectome during recovery from severe traumatic brain injury: protocol for a longitudinal prospective study

INTRODUCTION

Traumatic brain injury (TBI) is considered one of the most pervasive causes of disability in people under the age of 45. TBI often results in disorders of consciousness, and clinical assessment of the state of consciousness in these patients is challenging due to the lack of behavioural responsiveness. Functional neuroimaging offers a means to assess these patients without the need for behavioural signs, indicating that brain connectivity plays a major role in consciousness emergence and maintenance. However, little is known regarding how changes in connectivity during recovery from TBI accompany changes in the level of consciousness. Here, we aim to combine cutting-edge neuroimaging techniques to follow changes in brain connectivity in patients recovering from severe TBI.

METHODS AND ANALYSIS

A multimodal, longitudinal assessment of 30 patients in the subacute stage after severe TBI will be made comprising an MRI session combined with electroencephalography (EEG), a positron emission tomography session and a transcranial magnetic stimulation (TMS) combined with EEG (TMS/EEG) session. A group of 20 healthy participants will be included for comparison. Four sessions for patients and two sessions for healthy participants will be planned. Data analysis techniques will focus on whole-brain, both data-driven and hypothesis-driven, connectivity measures that will be specific to the imaging modality.

ETHICS AND DISSEMINATION

The project has received ethical approval by the local ethics committee of the Capital Region of Denmark and by the Danish Data Protection. Results will be published as original research articles in peer-reviewed journals and disseminated in international conferences. None of the measurements will have any direct clinical impact on the patients included in the study but may benefit future patients through a better understanding of the mechanisms underlying the recovery process after TBI. TRIAL REGISTRATION NUMBER NCT02424656; PRE-RESULTS.

Scale-Free Neural and Physiological Dynamics in Naturalistic Stimuli Processing

Neural activity recorded at multiple spatiotemporal scales is dominated by arrhythmic fluctuations without a characteristic temporal periodicity. Such activity often exhibits a 1/f-type power spectrum, in which power falls off with increasing frequency following a power-law function: [Formula: see text], which is indicative of scale-free dynamics. Two extensively studied forms of scale-free neural dynamics in the human brain are slow cortical potentials (SCPs)-the low-frequency (<5 Hz) component of brain field potentials-and the amplitude fluctuations of α oscillations, both of which have been shown to carry important functional roles. In addition, scale-free dynamics characterize normal human physiology such as heartbeat dynamics. However, the exact relationships among these scale-free neural and physiological dynamics remain unclear. We recorded simultaneous magnetoencephalography and electrocardiography in healthy subjects in the resting state and while performing a discrimination task on scale-free dynamical auditory stimuli that followed different scale-free statistics. We observed that long-range temporal correlation (captured by the power-law exponent β) in SCPs positively correlated with that of heartbeat dynamics across time within an individual and negatively correlated with that of α-amplitude fluctuations across individuals. In addition, across individuals, long-range temporal correlation of both SCP and α-oscillation amplitude predicted subjects' discrimination performance in the auditory task, albeit through antagonistic relationships. These findings reveal interrelations among different scale-free neural and physiological dynamics and initial evidence for the involvement of scale-free neural dynamics in the processing of natural stimuli, which often exhibit scale-free dynamics.

Analytical and empirical fluctuation functions of the EEG microstate random walk - Short-range vs. long-range correlations

We analyze temporal autocorrelations and the scaling behaviour of EEG microstate sequences during wakeful rest. We use the recently introduced random walk approach and compute its fluctuation function analytically under the null hypothesis of a short-range correlated, first-order Markov process. The empirical fluctuation function and the Hurst parameter H as a surrogate parameter of long-range correlations are computed from 32 resting state EEG recordings and for a set of first-order Markov surrogate data sets with equilibrium distribution and transition matrices identical to the empirical data. In order to distinguish short-range correlations (H ≈ 0.5) from previously reported long-range correlations (H > 0.5) statistically, confidence intervals for H and the fluctuation functions are constructed under the null hypothesis. Comparing three different estimation methods for H, we find that only one data set consistently shows H > 0.5, compatible with long-range correlations, whereas the majority of experimental data sets cannot be consistently distinguished from Markovian scaling behaviour. Our analysis suggests that the scaling behaviour of resting state EEG microstate sequences, though markedly different from uncorrelated, zero-order Markov processes, can often not be distinguished from a short-range correlated, first-order Markov process. Our results do not prove the microstate process to be Markovian, but challenge the approach to parametrize resting state EEG by single parameter models.

Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

Ongoing neuronal oscillations are pivotal in brain functioning and are known to influence subjects' performance. This modulation is usually studied on short time scales whilst multiple time scales are rarely considered. In our study we show that Long-Range Temporal Correlations (LRTCs) estimated from the amplitude of EEG oscillations over a range of time-scales predict performance in a complex sensorimotor task, based on Brain-Computer Interfacing (BCI). Our paradigm involved eighty subjects generating covert motor responses to dynamically changing visual cues and thus controlling a computer program through the modulation of neuronal oscillations. The neuronal dynamics were estimated with multichannel EEG. Our results show that: (a) BCI task accuracy may be predicted on the basis of LRTCs measured during the preceding training session, and (b) this result was not due to signal-to-noise ratio of the ongoing neuronal oscillations. Our results provide direct empirical evidence in addition to previous theoretical work suggesting that scale-free neuronal dynamics are important for optimal brain functioning.

Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement

The capacity of the human brain to interpret and respond to multiple temporal scales in its surroundings suggests that its internal interactions must also be able to operate over a broad temporal range. In this paper, we utilize a recently introduced method for characterizing the rate of change of the phase difference between MEG signals and use it to study the temporal structure of the phase interactions between MEG recordings from the left and right motor cortices during rest and during a finger-tapping task. We use the Hilbert transform to estimate moment-to-moment fluctuations of the phase difference between signals. After confirming the presence of scale-invariance we estimate the Hurst exponent using detrended fluctuation analysis (DFA). An exponent of >0.5 is indicative of long-range temporal correlations (LRTCs) in the signal. We find that LRTCs are present in the α/μ and β frequency bands of resting state MEG data. We demonstrate that finger movement disrupts LRTCs correlations, producing a phase relationship with a structure similar to that of Gaussian white noise. The results are validated by applying the same analysis to data with Gaussian white noise phase difference, recordings from an empty scanner and phase-shuffled time series. We interpret the findings through comparison of the results with those we obtained from an earlier study during which we adopted this method to characterize phase relationships within a Kuramoto model of oscillators in its sub-critical, critical, and super-critical synchronization states. We find that the resting state MEG from left and right motor cortices shows moment-to-moment fluctuations of phase difference with a similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling, and that finger tapping moves the system away from this pre-critical state toward a more random state.

Colored noise and memory effects on formal spiking neuron models

Simplified neuronal models capture the essence of the electrical activity of a generic neuron, besides being more interesting from the computational point of view when compared to higher-dimensional models such as the Hodgkin-Huxley one. In this work, we propose a generalized resonate-and-fire model described by a generalized Langevin equation that takes into account memory effects and colored noise. We perform a comprehensive numerical analysis to study the dynamics and the point process statistics of the proposed model, highlighting interesting new features such as (i) nonmonotonic behavior (emergence of peak structures, enhanced by the choice of colored noise characteristic time scale) of the coefficient of variation (CV) as a function of memory characteristic time scale, (ii) colored noise-induced shift in the CV, and (iii) emergence and suppression of multimodality in the interspike interval (ISI) distribution due to memory-induced subthreshold oscillations. Moreover, in the noise-induced spike regime, we study how memory and colored noise affect the coherence resonance (CR) phenomenon. We found that for sufficiently long memory, not only is CR suppressed but also the minimum of the CV-versus-noise intensity curve that characterizes the presence of CR may be replaced by a maximum. The aforementioned features allow to interpret the interplay between memory and colored noise as an effective control mechanism to neuronal variability. Since both variability and nontrivial temporal patterns in the ISI distribution are ubiquitous in biological cells, we hope the present model can be useful in modeling real aspects of neurons.

Relationship of fast- and slow-timescale neuronal dynamics in human MEG and SEEG

A growing body of evidence suggests that the neuronal dynamics are poised at criticality. Neuronal avalanches and long-range temporal correlations (LRTCs) are hallmarks of such critical dynamics in neuronal activity and occur at fast (subsecond) and slow (seconds to hours) timescales, respectively. The critical dynamics at different timescales can be characterized by their power-law scaling exponents. However, insight into the avalanche dynamics and LRTCs in the human brain has been largely obtained with sensor-level MEG and EEG recordings, which yield only limited anatomical insight and results confounded by signal mixing. We investigated here the relationship between the human neuronal dynamics at fast and slow timescales using both source-reconstructed MEG and intracranial stereotactical electroencephalography (SEEG). Both MEG and SEEG revealed avalanche dynamics that were characterized parameter-dependently by power-law or truncated-power-law size distributions. Both methods also revealed robust LRTCs throughout the neocortex with distinct scaling exponents in different functional brain systems and frequency bands. The exponents of power-law regimen neuronal avalanches and LRTCs were strongly correlated across subjects. Qualitatively similar power-law correlations were also observed in surrogate data without spatial correlations but with scaling exponents distinct from those of original data. Furthermore, we found that LRTCs in the autonomous nervous system, as indexed by heart-rate variability, were correlated in a complex manner with cortical neuronal avalanches and LRTCs in MEG but not SEEG. These scalp and intracranial data hence show that power-law scaling behavior is a pervasive but neuroanatomically inhomogeneous property of neuronal dynamics in central and autonomous nervous systems.

Long range temporal correlations in EEG oscillations of subclinically depressed individuals: their association with brooding and suppression

Long-range temporal correlations (LRTC) in brain oscillations have been found to be associated with depression severity in clinically depressed patients. Less is known, however, about the relationships between LRTC and proneness to engage in depression-related cognitive emotion regulation (ER) strategies which characterize both clinically and subclinically depressed (SBD) people. In this study we applied detrended fluctuation analysis to the amplitude envelope of broad band, theta band, and alpha band spontaneous EEG oscillations of a group of SBD individuals and a group of non-depressed individuals (both groups from a sample of healthy adults, N = 120), to whom brooding and thought suppression questionnaires were administered. Between-groups differences were not found for any band scaling exponents at any brain location, but linear correlations pointed out several associations between exponents at frontal, central, parietal, temporal, and occipital sites and maladaptive ER strategies. These results suggest that alterations in brain dynamics are related with the proneness that depressive individuals show to engage in brooding and thought suppression in order to cognitively regulate their emotions.

Existence of anticorrelations for local field potentials recorded from mice reared in standard condition and environmental enrichment

In the present paper, we analyze local field potentials (LFPs) recorded from the secondary motor cortex (M2) and primary visual cortex (V1) of freely moving mice reared in environmental enrichment (EE) and standard condition (SC). We focus on the scaling properties of the signals by using an integrated approach combining three different techniques: the Higuchi method, detrended fluctuation analysis, and power spectrum. Each technique provides direct or indirect estimations of the Hurst exponent H and this prevents spurious identification of scaling properties in time-series analysis. It is well known that the power spectrum of an LFP signal scales as 1/f(β) with β>0. Our results indicate the existence of a particular power spectrum scaling law 1/f(β) with β<0 for low frequencies (f<4 Hz) for both SC and EE rearing conditions. This type of scaling behavior is associated to the presence of anticorrelation in the corresponding LFP signals. Moreover, since EE is an experimental protocol based on the enhancement of sensorimotor stimulation, we study the possible effects of EE on the scaling properties of secondary motor cortex (M2) and primary visual cortex (V1). Notably, the difference between Hurst's exponents in EE and SC for individual cortical regions (M2) and (V1) is not statistically significant. On the other hand, using the detrended cross-correlation coefficient, we find that EE significantly reduces the functional coupling between secondary motor cortex (M2) and visual cortex (V1).

Long-range correlation properties in timing of skilled piano performance: the influence of auditory feedback and deep brain stimulation

Unintentional timing deviations during musical performance can be conceived of as timing errors. However, recent research on humanizing computer-generated music has demonstrated that timing fluctuations that exhibit long-range temporal correlations (LRTC) are preferred by human listeners. This preference can be accounted for by the ubiquitous presence of LRTC in human tapping and rhythmic performances. Interestingly, the manifestation of LRTC in tapping behavior seems to be driven in a subject-specific manner by the LRTC properties of resting-state background cortical oscillatory activity. In this framework, the current study aimed to investigate whether propagation of timing deviations during the skilled, memorized piano performance (without metronome) of 17 professional pianists exhibits LRTC and whether the structure of the correlations is influenced by the presence or absence of auditory feedback. As an additional goal, we set out to investigate the influence of altering the dynamics along the cortico-basal-ganglia-thalamo-cortical network via deep brain stimulation (DBS) on the LRTC properties of musical performance. Specifically, we investigated temporal deviations during the skilled piano performance of a non-professional pianist who was treated with subthalamic-deep brain stimulation (STN-DBS) due to severe Parkinson's disease, with predominant tremor affecting his right upper extremity. In the tremor-affected right hand, the timing fluctuations of the performance exhibited random correlations with DBS OFF. By contrast, DBS restored long-range dependency in the temporal fluctuations, corresponding with the general motor improvement on DBS. Overall, the present investigations demonstrate the presence of LRTC in skilled piano performances, indicating that unintentional temporal deviations are correlated over a wide range of time scales. This phenomenon is stable after removal of the auditory feedback, but is altered by STN-DBS, which suggests that cortico-basal ganglia-thalamocortical circuits play a role in the modulation of the serial correlations of timing fluctuations exhibited in skilled musical performance.

Markers of criticality in phase synchronization

The concept of the brain as a critical dynamical system is very attractive because systems close to criticality are thought to maximize their dynamic range of information processing and communication. To date, there have been two key experimental observations in support of this hypothesis: (i) neuronal avalanches with power law distribution of size and (ii) long-range temporal correlations (LRTCs) in the amplitude of neural oscillations. The case for how these maximize dynamic range of information processing and communication is still being made and because a significant substrate for information coding and transmission is neural synchrony it is of interest to link synchronization measures with those of criticality. We propose a framework for characterizing criticality in synchronization based on an analysis of the moment-to-moment fluctuations of phase synchrony in terms of the presence of LRTCs. This framework relies on an estimation of the rate of change of phase difference and a set of methods we have developed to detect LRTCs. We test this framework against two classical models of criticality (Ising and Kuramoto) and recently described variants of these models aimed to more closely represent human brain dynamics. From these simulations we determine the parameters at which these systems show evidence of LRTCs in phase synchronization. We demonstrate proof of principle by analysing pairs of human simultaneous EEG and EMG time series, suggesting that LRTCs of corticomuscular phase synchronization can be detected in the resting state and experimentally manipulated. The existence of LRTCs in fluctuations of phase synchronization suggests that these fluctuations are governed by non-local behavior, with all scales contributing to system behavior. This has important implications regarding the conditions under which one should expect to see LRTCs in phase synchronization. Specifically, brain resting states may exhibit LRTCs reflecting a state of readiness facilitating rapid task-dependent shifts toward and away from synchronous states that abolish LRTCs.

Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ⁹-tetrahydrocannabinol administration

BACKGROUND

Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing.

NEW METHOD

Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models.

RESULTS

Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons.

COMPARISON WITH EXISTING METHODS

WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events.

CONCLUSION

z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces.

Functional significance of complex fluctuations in brain activity: from resting state to cognitive neuroscience

Behavioral studies have shown that human cognition is characterized by properties such as temporal scale invariance, heavy-tailed non-Gaussian distributions, and long-range correlations at long time scales, suggesting models of how (non observable) components of cognition interact. On the other hand, results from functional neuroimaging studies show that complex scaling and intermittency may be generic spatio-temporal properties of the brain at rest. Somehow surprisingly, though, hardly ever have the neural correlates of cognition been studied at time scales comparable to those at which cognition shows scaling properties. Here, we analyze the meanings of scaling properties and the significance of their task-related modulations for cognitive neuroscience. It is proposed that cognitive processes can be framed in terms of complex generic properties of brain activity at rest and, ultimately, of functional equations, limiting distributions, symmetries, and possibly universality classes characterizing them.

Scale-free brain activity: past, present, and future

Brain activity observed at many spatiotemporal scales exhibits a 1/f-like power spectrum, including neuronal membrane potentials, neural field potentials, noninvasive electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) signals. A 1/f-like power spectrum is indicative of arrhythmic brain activity that does not contain a predominant temporal scale (hence, 'scale-free'). This characteristic of scale-free brain activity distinguishes it from brain oscillations. Although scale-free brain activity and brain oscillations coexist, our understanding of the former remains limited. Recent research has shed light on the spatiotemporal organization, functional significance, and potential generative mechanisms of scale-free brain activity, as well as its developmental and clinical relevance. A deeper understanding of this prevalent brain signal should provide new insights into, and analytical tools for, cognitive neuroscience.

Identification of Criticality in Neuronal Avalanches: II. A Theoretical and Empirical Investigation of the Driven Case

The observation of apparent power laws in neuronal systems has led to the suggestion that the brain is at, or close to, a critical state and may be a self-organised critical system. Within the framework of self-organised criticality a separation of timescales is thought to be crucial for the observation of power-law dynamics and computational models are often constructed with this property. However, this is not necessarily a characteristic of physiological neural networks-external input does not only occur when the network is at rest/a steady state. In this paper we study a simple neuronal network model driven by a continuous external input (i.e. the model does not have an explicit separation of timescales from seeding the system only when in the quiescent state) and analytically tuned to operate in the region of a critical state (it reaches the critical regime exactly in the absence of input-the case studied in the companion paper to this article). The system displays avalanche dynamics in the form of cascades of neuronal firing separated by periods of silence. We observe partial scale-free behaviour in the distribution of avalanche size for low levels of external input. We analytically derive the distributions of waiting times and investigate their temporal behaviour in relation to different levels of external input, showing that the system's dynamics can exhibit partial long-range temporal correlations. We further show that as the system approaches the critical state by two alternative 'routes', different markers of criticality (partial scale-free behaviour and long-range temporal correlations) are displayed. This suggests that signatures of criticality exhibited by a particular system in close proximity to a critical state are dependent on the region in parameter space at which the system (currently) resides.

Prestimulus oscillatory power and connectivity patterns predispose conscious somatosensory perception

Which aspects of our sensory environment enter conscious awareness does not only depend on physical features of the stimulus, but also critically on the so-called current brain state. Results from magnetoencephalography/EEG studies using near-threshold stimuli have consistently pointed to reduced levels of α- (8-12 Hz) power in relevant sensory areas to predict whether a stimulus will be consciously perceived or not. These findings have been mainly interpreted in strictly "local" terms of enhanced excitability of neuronal ensembles in respective cortical regions. The present study aims to introduce a framework that complements this rather local perspective, by stating that the functional connectivity architecture before stimulation will predetermine information flow. Thus, information computed at a local level will be distributed throughout a network, thereby becoming consciously accessible. Data from a previously published experiment on conscious somatosensory near-threshold perception was reanalyzed focusing on the prestimulus period. Analysis of spectral power showed reduced α-power mainly in the contralateral S2 and middle frontal gyrus to precede hits, thus overall supporting the current literature. Furthermore, differences between hits and misses were obtained on global network (graph theoretical) features in the same interval. Most importantly, in accordance with our framework, we could show that the somatosensory cortex is "more efficiently" integrated into a distributed network in the prestimulus period. This finding means that when a relevant sensory stimulus impinges upon the system, it will encounter preestablished pathways for information flow. In this sense, prestimulus functional connectivity patterns form "windows" to conscious perception.

Long-range temporal correlations in resting-state α oscillations predict human timing-error dynamics

Human behavior is imperfect. This is notably clear during repetitive tasks in which sequences of errors or deviations from perfect performance result. These errors are not random, but show patterned fluctuations with long-range temporal correlations that are well described using power-law spectra P(f)∝1/f(β), where β is the power-law scaling exponent describing the decay in temporal correlations. The neural basis of temporal correlations in such behaviors is not known. Interestingly, long-range temporal correlations are a hallmark of amplitude fluctuations in resting-state neuronal oscillations. Here, we investigated whether the temporal dynamics in brain and behavior are related. Thirty-nine subjects' eyes-open rest EEG was measured. Next, subjects reproduced without feedback a 1 s interval by tapping with their right index finger. In line with previous reports, we found evidence for the presence of long-range temporal correlations both in the amplitude modulation of resting-state oscillations in multiple frequency bands and in the timing-error sequences. Frequency scaling exponents of finger tapping and amplitude modulation of oscillations exhibited large individual differences. Neuronal dynamics of resting-state alpha-band oscillations (9-13 Hz) recorded at precentral sites strongly predicted scaling exponents of tapping behavior. The results suggest that individual variation in resting-state brain dynamics offer a neural explanation for individual variation in the error dynamics of human behavior.