Understanding Of Brain Dynamics Research Articles

Whole-brain modelling: an essential tool for understanding brain dynamics

Whole-brain modelling: an essential tool for understanding brain dynamics

Brain signaling becomes less integrated and more segregated with age

Abstract The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, Pint and Pseg, from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.

Hypergraph of Functional Connectivity Based on Event-Related Coherence: Magnetoencephalography Data Analysis

We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for the delta, theta, alpha, beta, and gamma frequency ranges. Employing a coherence measure, a statistical estimate of correlation between signal pairs across frequencies, we generate an edge time series, depicting how an edge evolves over time. This forms the basis for constructing an edge-to-edge functional connectivity network. We emphasize hyperedges as connected components in an absolute-valued functional connectivity network. Our coherence-based hypergraph construction specifically addresses functional connectivity among four brain lobes in both hemispheres: frontal, parietal, temporal, and occipital. This approach enables a nuanced exploration of individual differences within diverse frequency bands, providing insights into the dynamic nature of brain connectivity during visual perception tasks. The results furnish compelling evidence supporting the hypothesis of cortico–cortical interactions occurring across varying scales. The derived hypergraph illustrates robust activation patterns in specific brain regions, indicative of their engagement across diverse cognitive contexts and different frequency bands. Our findings suggest potential integration or multifunctionality within the examined lobes, contributing valuable perspectives to our understanding of brain dynamics during visual perception.

Distinct neocortical mechanisms underlie human SI responses to median nerve and laser-evoked peripheral activation

Abstract Magneto- and/or electro-encephalography (M/EEG) are non-invasive clinically relevant tools that have long been used to measure electromagnetic fields in the somatosensory cortex evoked by innocuous and noxious somatosensory stimuli. Two commonly applied stimulation paradigms that produce distinct responses in the primary somatosensory cortex (SI) linked to innocuous and noxious sensations are electrical median nerve (MN) stimulation and cutaneous laser-evoked (LE) stimulation to the dorsum of the hand, respectively. Despite their prevalence, the physiological mechanisms that produce stereotypic macroscale MN and LE responses have yet to be fully articulated, limiting their utility in understanding brain dynamics associated with non-painful and/or painful somatosensation. Through a literature review, we detailed features of MN and LE responses source-localized to SI that are robust and reproducible across studies. We showed that the first peak in the MN response at ~20 ms post-stimulus (i.e., MN N1) corresponds to upward-directed deep-to-superficial electrical current flow through the cortical laminae, which is followed by downward-directed current at ~30 ms (i.e., MN P1). In contrast, the initial LE response occurs later at ~170 ms (i.e., LE N1) and is directed downward and opposite the direction of the MN N1. We then examined the neocortical circuit mechanisms contributing to the robust features of each response using the Human Neocortical Neurosolver (HNN) neural modeling software tool (Neymotin et al., 2020). Using HNN as a hypothesis development and testing tool, model results predicted the MN response can be simulated with a sequence of layer-specific thalamocortical and cortico-cortical synaptic drive similar to that previously reported for tactile evoked responses (S. R. Jones et al., 2007; Neymotin et al., 2020), with the novel discovery that an early excitatory input to supragranular layers at ~30 ms is an essential mechanism contributing to the downward current flow of the MN P1. Model results further predicted that the initial ~170 ms downward current flow of the LE N1 was generated by a burst of repetitive gamma-frequency (~40 Hz) excitatory synaptic drive to supragranular layers, consistent with prior reports of LE gamma-frequency activity. These results make novel and detailed multiscale predictions about the dynamic laminar circuit mechanisms underlying temporal and spectral features of MN and LE responses in SI and can guide further investigations in follow-up studies. Ultimately, these findings may help with the development of targeted therapeutics for pathological somatosensation, such as somatic sensitivity and acute neuropathic pain.

Bi-sinusoidal light stimulation reveals an enhanced response power and reduced phase coherence at the visual cortex in migraine.

Migraine is associated with enhanced visual sensitivity during and outside attacks. Processing of visual information is a highly non-linear process involving complex interactions across (sub)cortical networks. In this exploratory study, we combined electroencephalography with bi-sinusoidal light stimulation to assess non-linear features of visual processing in participants with migraine. Twenty participants with migraine (10 with aura, 10 without aura) and ten non-headache controls were measured (outside attacks). Participants received bi-sinusoidal 13 + 23 Hz red light visual stimulation. Electroencephalography spectral power and multi-spectral phase coherence were compared between groups at the driving stimulation frequencies together with multiples and combinations of these frequencies (harmonic and intermodulation frequencies) caused by non-linearities. Only at the driving frequency of 13 Hz higher spectral power was found in migraine with aura participants compared with those with migraine without aura and controls. Differences in phase coherence were present for 2nd, 4th, and 5th-order non-linearities in those with migraine (migraine with and without aura) compared with controls. Bi-sinusoidal light stimulation revealed evident non-linearities in the brain's electroencephalography response up to the 5th order with reduced phase coherence for higher order interactions in interictal participants with migraine. Insight into interictal non-linear visual processing may help understand brain dynamics underlying migraine attack susceptibility. Future research is needed to determine the clinical value of the results.

Dose-dependent changes in orientation amplitude maps in the cat visual cortex after propofol bolus injections

A general intravenous anesthetic propofol (2,6-diisopropylphenol) is widely used in clinical, veterinary practice and animal experiments. It activates gamma- aminobutyric acid (GABAa) receptors. Though the cerebral cortex is one of the major targets of propofol action, no study of dose dependency of propofol action on cat visual cortex was performed yet. Also, no such investigation was done until now using intrinsic signal optical imaging. Here, we report for the first time on the dependency of optical signal in the visual cortex (area 17/area 18) on the propofol dose. Optical imaging of intrinsic responses to visual stimuli was performed in cats before and after propofol bolus injections at different doses on the background of continuous propofol infusion. Orientation amplitude maps were recorded. We found that amplitude of optical signal significantly decreased after a bolus dose of propofol. The effect was dose- and time-dependent producing stronger suppression of optical signal under the highest bolus propofol doses and short time interval after injection. In each hemisphere, amplitude at cardinal and oblique orientations decreased almost equally. However, surprisingly, amplitude at cardinal orientations in the ipsilateral hemisphere was depressed stronger than in contralateral cortex at most time intervals. As the magnitude of optical signal represents the strength of orientation tuned component, these our data give new insights on the mechanisms of generation of orientation selectivity. Our results also provide new data toward understanding brain dynamics under anesthesia and suggest a recommendation for conducting intrinsic signal optical imaging experiments on cortical functioning under propofol anesthesia.

Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage.

Decoding of cognitive states aims to identify individuals' brain states and brain fingerprints to predict behavior. Deep learning provides an important platform for analyzing brain signals at different developmental stages to understand brain dynamics. Due to their internal architecture and feature extraction techniques, existing machine-learning and deep-learning approaches are suffering from low classification performance and explainability issues that must be improved. In the current study, we hypothesized that even at the early childhood stage (as early as 3-years), connectivity between brain regions could decode brain states and predict behavioral performance in false-belief tasks. To this end, we proposed an explainable deep learning framework to decode brain states (Theory of Mind and Pain states) and predict individual performance on ToM-related false-belief tasks in a developmental dataset. We proposed an explainable spatiotemporal connectivity-based Graph Convolutional Neural Network (Ex-stGCNN) model for decoding brain states. Here, we consider a developmental dataset, N = 155 (122 children; 3-12 yrs and 33 adults; 18-39 yrs), in which participants watched a short, soundless animated movie, shown to activate Theory-of-Mind (ToM) and pain networs. After scanning, the participants underwent a ToM-related false-belief task, leading to categorization into the pass, fail, and inconsistent groups based on performance. We trained our proposed model using Functional Connectivity (FC) and Inter-Subject Functional Correlations (ISFC) matrices separately. We observed that the stimulus-driven feature set (ISFC) could capture ToM and Pain brain states more accurately with an average accuracy of 94%, whereas it achieved 85% accuracy using FC matrices. We also validated our results using five-fold cross-validation and achieved an average accuracy of 92%. Besides this study, we applied the SHapley Additive exPlanations (SHAP) approach to identify brain fingerprints that contributed the most to predictions. We hypothesized that ToM network brain connectivity could predict individual performance on false-belief tasks. We proposed an Explainable Convolutional Variational Auto-Encoder (Ex-Convolutional VAE) model to predict individual performance on false-belief tasks and trained the model using FC and ISFC matrices separately. ISFC matrices again outperformed the FC matrices in prediction of individual performance. We achieved 93.5% accuracy with an F1-score of 0.94 using ISFC matrices and achieved 90% accuracy with an F1-score of 0.91 using FC matrices.

Towards Implantable Brain-Computer Interface for Communication in Locked-In Syndrome patients

Abstract Locked-in Syndrome (LIS) severely restricts the communication abilities of individuals due to extensive paralysis. The Intracranial Neuro Telemetry to Restore communication (INTRECOM) project aims to aid patients in overcoming these limitations by developing a fully implantable brain computer interface (BCI) system based on state-of-the-art technology holding great promise in revolutionizing the lives of LIS patients. In this project, the Graz BCI group, with its expertise in understanding brain dynamics associated with movement, focuses on the algorithmic development of advanced decoders that enable the user to execute specific commands by simply attempting corresponding movements. Our preliminary findings using electrocorticography (ECoG) data obtained from one individual with LIS that underwent implantation of a BCI communication system demonstrate the applicability of a ‘brain switch’ function that detects brain signals associated with attempted movements. This switch can then be used to translate the intention of the user into a click/select function on a screen.

Time-of-day influences resting-state functional cortical connectivity.

Time-of-day is rarely considered during experimental protocols investigating motor behavior and neural activity. The goal of this work was to investigate differences in functional cortical connectivity at rest linked to the time of the day using functional Near-Infrared Spectroscopy (fNIRS). Since resting-state brain is shown to be a succession of cognitive, emotional, perceptual, and motor processes that can be both conscious and nonconscious, we studied self-generated thought with the goal to help in understanding brain dynamics. We used the New-York Cognition Questionnaire (NYC-Q) for retrospective introspection to explore a possible relationship between the ongoing experience and the brain at resting-state to gather information about the overall ongoing experience of subjects. We found differences in resting-state functional connectivity in the inter-hemispheric parietal cortices, which was significantly greater in the morning than in the afternoon, whilst the intra-hemispheric fronto-parietal functional connectivity was significantly greater in the afternoon than in the morning. When we administered the NYC-Q we found that the score of the question 27 ("during RS acquisition my thoughts were like a television program or film") was significantly greater in the afternoon with respect to the morning. High scores in question 27 point to a form of thought based on imagery. It is conceivable to think that the unique relationship found between NYC-Q question 27 and the fronto-parietal functional connectivity might be related to a mental imagery process during resting-state in the afternoon.

Extracting continuous sleep depth from EEG data without machine learning

The human sleep-cycle has been divided into discrete sleep stages that can be recognized in electroencephalographic (EEG) and other bio-signals by trained specialists or machine learning systems. It is however unclear whether these human-defined stages can be re-discovered with unsupervised methods of data analysis, using only a minimal amount of generic pre-processing. Based on EEG data, recorded overnight from sleeping human subjects, we investigate the degree of clustering of the sleep stages using the General Discrimination Value as a quantitative measure of class separability. Virtually no clustering is found in the raw data, even after transforming the EEG signals of each 30-s epoch from the time domain into the more informative frequency domain. However, a Principal Component Analysis (PCA) of these epoch-wise frequency spectra reveals that the sleep stages separate significantly better in the low-dimensional sub-space of certain PCA components. In particular the component C1(t) can serve as a robust, continuous ‘master variable’ that encodes the depth of sleep and therefore correlates strongly with the ‘hypnogram’, a common plot of the discrete sleep stages over time. Moreover, C1(t) shows persistent trends during extended time periods where the sleep stage is constant, suggesting that sleep may be better understood as a continuum. These intriguing properties of C1(t) are not only relevant for understanding brain dynamics during sleep, but might also be exploited in low-cost single-channel sleep tracking devices for private and clinical use.

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions.

Many state-of-the-art methods for seizure prediction, using the electroencephalogram, are based on machine learning models that are black boxes, weakening the trust of clinicians in them for high-risk decisions. Seizure prediction concerns a multidimensional time-series problem that performs continuous sliding window analysis and classification. In this work, we make a critical review of which explanations increase trust in models' decisions for predicting seizures. We developed three machine learning methodologies to explore their explainability potential. These contain different levels of model transparency: a logistic regression, an ensemble of 15 support vector machines, and an ensemble of three convolutional neural networks. For each methodology, we evaluated quasi-prospectively the performance in 40 patients (testing data comprised 2055 hours and 104 seizures). We selected patients with good and poor performance to explain the models' decisions. Then, with grounded theory, we evaluated how these explanations helped specialists (data scientists and clinicians working in epilepsy) to understand the obtained model dynamics. We obtained four lessons for better communication between data scientists and clinicians. We found that the goal of explainability is not to explain the system's decisions but to improve the system itself. Model transparency is not the most significant factor in explaining a model decision for seizure prediction. Even when using intuitive and state-of-the-art features, it is hard to understand brain dynamics and their relationship with the developed models. We achieve an increase in understanding by developing, in parallel, several systems that explicitly deal with signal dynamics changes that help develop a complete problem formulation.

The Effects of Directional and Non-Directional Stimuli during a Visuomotor Task and Their Correlation with Reaction Time: An ERP Study

Different visual stimuli can capture and shift attention into different directions. Few studies have explored differences in brain response due to directional (DS) and non-directional visual stimuli (nDS). To explore the latter, event-related potentials (ERP) and contingent negative variation (CNV) during a visuomotor task were evaluated in 19 adults. To examine the relation between task performance and ERPs, the participants were divided into faster (F) and slower (S) groups based on their reaction times (RTs). Moreover, to reveal ERP modulation within the same subject, each recording from the single participants was subdivided into F and S trials based on the specific RT. ERP latencies were analysed between conditions ((DS, nDS); (F, S subjects); (F, S trials)). Correlation was analysed between CNV and RTs. Our results reveal that the ERPs' late components are modulated differently by DS and nDS conditions in terms of amplitude and location. Differences in ERP amplitude, location and latency, were also found according to subjects' performance, i.e., between F and S subjects and trials. In addition, results show that the CNV slope is modulated by the directionality of the stimulus and contributes to motor performance. A better understanding of brain dynamics through ERPs could be useful to explain brain states in healthy subjects and to support diagnoses and personalized rehabilitation in patients with neurological diseases.

Test-retest reliability of time-varying patterns of brain activity across single band and multiband resting-state functional magnetic resonance imaging in healthy older adults.

Leading Eigenvector Dynamics Analysis (LEiDA) is an analytic approach that characterizes brain activity recorded with functional Magnetic Resonance Imaging (fMRI) as a succession of discrete phase-locking patterns, or states, that consistently recur over time across all participants. LEiDA allows for the extraction of three state-related measures which have previously been key to gaining a better understanding of brain dynamics in both healthy and clinical populations: the probability of occurrence of a given state, its lifetime and the probability of switching from one state to another. The degree to which test-retest reliability of the LEiDA measures may be affected by increasing MRI multiband (MB) factors in comparison with single band sequences is yet to be established. In this study, 24 healthy older adults were scanned over three sessions, on weeks 0, 1, and 4. On each visit, they underwent a conventional single band resting-state fMRI (rs-fMRI) scan and three different MB rs-fMRI scans, with MB factors of 4, with and without in-plane acceleration, and 6 without in-plane acceleration. We found test-retest reliability scores to be significantly higher with MB factor 4 with and without in-plane acceleration for most cortical networks. These findings will inform the choice of acquisition parameters for future studies and clinical trials.

Analytic modeling of neural tissue: II. Nonlinear membrane dynamics.

Computational modeling of neuroactivity plays a central role in our effort to understand brain dynamics in the advancements of neural engineering such as deep brain stimulation, neuroprosthetics, and magnetic resonance electrical impedance tomography. However, analytic solutions do not capture the fundamental nonlinear behavior of an action potential. What is needed is a method that is not constrained to only linearized models of neural tissue. Therefore, the objective of this study is to establish a robust, straightforward process for modeling neurodynamic phenomena, which preserves their nonlinear features. To address this, we turn to decomposition methods from homotopy analysis, which have emerged in recent decades as powerful tools for solving nonlinear differential equations. We solve the nonlinear ordinary differential equations of three landmark models of neural conduction-Ermentrout-Kopell, FitzHugh-Nagumo, and Hindmarsh-Rose models-using George Adomian's decomposition method. For each variable, we construct a power series solution equivalent to a generalized Taylor series expanded about a function. The first term of the decomposition series comes from the models' initial conditions. All subsequent terms are recursively determined from the first. We show rapid convergence, achieving a maximal error of with only eight terms. We extend the region of convergence with one-step analytic continuation so that our complete solutions are decomposition splines. We show that this process can yield solutions for single- and multi-variable models and can characterize a single action potential or complex bursting patterns. Finally, we show that the accuracy of this decomposition approach favorably compares to an established polynomial method, B-spline collocation. The strength of this method, besides its stability and ease of computation, is that, unlike perturbation, we make no changes to the models' equations; thus, our solutions are to the problems at hand, not simplified versions. This work validates decomposition as a viable technique for advanced neural engineering studies.

Complexity analysis of heartbeat-related signals in brain MRI time series as a potential biomarker for ageing and cognitive performance

Getting older affects both the structure of the brain and some cognitive capabilities. Until now, magnetic resonance imaging (MRI) approaches have been unable to give a coherent reflection of the cognitive declines. It shows the limitation of the contrast mechanisms used in most MRI investigations, which are indirect measures of brain activities depending on multiple physiological and cognitive variables. However, MRI signals may contain information of brain activity beyond these commonly used signals caused by the neurovascular response. Here, we apply a zero-spin echo (ZSE) weighted MRI sequence, which can detect heartbeat-evoked signals (HES). Remarkably, these MRI signals have properties only known from electrophysiology. We investigated the complexity of the HES arising from this sequence in two age groups; young (18–29 years) and old (over 65 years). While comparing young and old participants, we show that the complexity of the HES decreases with age, where the stability and chaoticity of these HES are particularly sensitive to age. However, we also found individual differences which were independent of age. Complexity measures were related to scores from different cognitive batteries and showed that higher complexity may be related to better cognitive performance. These findings underpin the affinity of the HES to electrophysiological signals. The profound sensitivity of these changes in complexity shows the potential of HES for understanding brain dynamics that need to be tested in more extensive and diverse populations with clinical relevance for all neurovascular diseases.

Learning Brain Dynamics of Evolving Manifold Functional MRI Data Using Geometric-Attention Neural Network.

Functional connectivities (FC) of brain network manifest remarkable geometric patterns, which is the gateway to understanding brain dynamics. In this work, we present a novel geometric-attention neural network to characterize the time-evolving brain state change from the functional neuroimages by tracking the trajectory of functional dynamics on high-dimension Riemannian manifold of symmetric positive definite (SPD) matrices. Specifically, we put the spotlight on learning the common state-specific manifold signatures that represent the underlying cognition. In this context, the driving force of our neural network is tied up with the learning of the evolution functionals on the Riemannian manifold of SPD matrix that underlies the known evolving brain states. To do so, we train a convolution neural network (CNN) on the Riemannian manifold of SPD matrices to seek for the putative low-dimension feature representations, followed by an end-to-end recurrent neural network (RNN) to yield the time-varying mapping function of SPD matrices which fits the evolutionary trajectories of the underlying states. Furthermore, we devise a geometric attention mechanism in CNN, allowing us to discover the latent geometric patterns in SPD matrices that are associated with the underlying states. Notably, our work has the potential to understand how brain function emerges behavior by investigating the geometrical patterns from functional brain networks, which is essentially a correlation matrix of neuronal activity signals. Our proposed manifold-based neural network achieves promising results in predicting brain state changes on both simulated data and task functional neuroimaging data from Human Connectome Project, which implies great applicability in neuroscience studies.

Electroencephalogram microstates and functional connectivity of cybersickness.

Virtual reality (VR) is a rapidly developing technology that simulates the real world. However, for some cybersickness-susceptible people, VR still has an unanswered problem—cybersickness—which becomes the main obstacle for users and content makers. Sensory conflict theory is a widely accepted theory for cybersickness. It proposes that conflict between afferent signals and internal models can cause cybersickness. This study analyzes the brain states that determine cybersickness occurrence and related uncomfortable feelings. Furthermore, we use the electroencephalogram (EEG) microstates and functional connectivity approach based on the sensory conflict theory. The microstate approach is a time–space analysis method that allows signals to be divided into several temporarily stable states, simultaneously allowing for the exploration of short- and long-range signals. These temporal dynamics can show the disturbances in mental processes associated with neurological and psychiatric conditions of cybersickness. Furthermore, the functional connectivity approach gives us in-depth insight and relationships between the sources related to cybersickness. We recruited 40 males (24.1 ± 2.3 years), and they watched a VR video on a curved computer monitor for 10 min to experience cybersickness. We recorded the 5-min resting state EEG (baseline condition) and 10-min EEG while watching the VR video (task condition). Then, we performed a microstate analysis, focusing on two temporal parameters: mean duration and global explained variance (GEV). Finally, we obtained the functional connectivity data using eLoreta and lagged phase synchronization (LPS). We discovered five sets of microstates (A–E), including four widely reported canonical microstates (A–D), during baseline and task conditions. The average duration increased in microstates A and B, which is related to the visual and auditory networks. The GEV and duration decreased in microstate C, whereas those in microstate D increased. Microstate C is related to the default mode network (DMN) and D to the attention network. The temporal dynamics of the microstate parameters are from cybersickness disturbing the sensory, DMN, and attention networks. In the functional connectivity part, the LPS between the left and right parietal operculum (OP) significantly decreased (p < 0.05) compared with the baseline condition. Furthermore, the connectivity between the right OP and V5 significantly decreased (p < 0.05). These results also support the disturbance of the sensory network because a conflict between the visual (V5) and vestibular system (OP) causes cybersickness. Changes in the microstates and functional connectivity support the sensory conflict theory. These results may provide additional information in understanding brain dynamics during cybersickness.

Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study.

Brain-computer interfaces (BCI) that enables people with severe motor disabilities to use their brain signals for direct control of objects have attracted increased interest in rehabilitation. To date, no study has investigated feasibility of the BCI framework incorporating both intracortical and scalp signals. Concurrent local field potential (LFP) from the hand-knob area and scalp EEG were recorded in a paraplegic patient undergoing a spike-based close-loop neurorehabilitation training. Based upon multimodal spatio-spectral feature extraction and Naïve Bayes classification, we developed, for the first time, a novel LFP-EEG-BCI for motor intention decoding. A transfer learning (TL) approach was employed to further improve the feasibility. The performance of the proposed LFP-EEG-BCI for four-class upper-limb motor intention decoding was assessed. Using a decision fusion strategy, we showed that the LFP-EEG-BCI significantly (p 0.05) outperformed single modal BCI (LFP-BCI and EEG-BCI) in terms of decoding accuracy with the best performance achieved using regularized common spatial pattern features. Interrogation of feature characteristics revealed discriminative spatial and spectral patterns, which may lead to new insights for better understanding of brain dynamics during different motor imagery tasks and promote development of efficient decoding algorithms. Moreover, we showed that similar classification performance could be obtained with few training trials, therefore highlighting the efficacy of TL. The present findings demonstrated the superiority of the novel LFP-EEG-BCI in motor intention decoding. This work introduced a novel LFP-EEG-BCI that may lead to new directions for developing practical neurorehabilitation systems with high detection accuracy and multi-paradigm feasibility in clinical applications.

A novel approach to the study of spatio-temporal brain dynamics using change-based complexity

Brain complexity and neural oscillations are of vital significance for understanding brain dynamics. Although widely used in the quantification of complex behavior of the brain, standard complexity measures are hampered by some theoretical and implementation issues which limit their applicability to the study of nonlinear brain signals. Further, they have been used for spatial analysis but are insensitive to the temporal structure of data and not applicable to short EEG segments. We demonstrated the ability of a change-based complexity measure (Aksentijevic-Gibson complexity) to analyze neural activity in both spatial and temporal domains. We examined EEG recordings from a group of adolescents with schizophrenia and an age-matched group of healthy controls. AG revealed that the spatial complexity was significantly lower in the patient group across all electrodes with the lowest complexity at the frontal area of the brain. By focusing on outer frontal regions and temporal data averaged over schizophrenia adolescents, we observed periodic drops in complexity and low-delta oscillations at the left electrode F7 and numerous irregularly placed drops at the right side (F8). Periodicity was caused by zero complexity troughs in several individuals associated with large voltage declensions and connected with hypofrontality supporting left frontal pathology. Both averaged spatial and temporal complexities of healthy and schizophrenia adolescents were significantly different from the complexities of random sequences confirming that neural activity is more regular than a true random process. As a final step, we performed the analysis of short EEG segments for the two subjects (one control, one patient) in order to search for differences in neural oscillations. The examination revealed a clear 33 Hz gamma rhythm in a healthy participant and only a fast beta rhythm of 27 Hz in a patient as well as the presence of a full range of rhythms in a healthy EEG and only theta oscillations in that of a patient. The results support the large body of work which associates schizophrenia with lowered complexity, low-frequency rhythms and the scarcity of high-frequency oscillations.

Criticality in Spreading Processes without Timescale Separation and the Critical Brain Hypothesis

Spreading processes on networks are ubiquitous in both human-made and natural systems. Understanding their behavior is of broad interest: from the control of epidemics to understanding brain dynamics. While in some cases there exists a clear separation of timescales between the propagation of a single spreading cascade and the initiation of the next—such that spreading can be modeled as directed percolation or a branching process—there are also processes for which this is not the case, such as spiking cascades in neural networks. For a large class of relevant network topologies, we show here that in such a scenario the nature of the overall spreading fundamentally changes. This change manifests itself in a transition between different universality classes of critical behavior, which determines the onset and the properties of neural activity turning epileptic, for example. We present analytical results in the mean-field limit, giving the critical line along which scale-free behavior can be observed. The two limits of this critical line correspond to the universality classes of directed and undirected percolation, respectively. Outside these two limits, this duality manifests itself in the appearance of critical exponents from the universality classes of both directed and undirected percolation. We find that the transition between these exponents is governed by a competition between merging and propagation of activity, and we identify an appropriate scaling relationship for the transition point. Finally, we show that commonly used measures, such as the branching ratio and dynamic susceptibility, fail to establish criticality in the absence of timescale separation, calling for a reanalysis of criticality in the brain.10 MoreReceived 5 June 2020Revised 22 February 2021Accepted 12 April 2021Corrected 25 June 2021DOI:https://doi.org/10.1103/PhysRevX.11.021059Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNetwork phase transitionsNeuronal avalanchesNonequilibrium statistical mechanicsSpreadingNetworksStatistical PhysicsBiological Physics