Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

Spatiotemporal dynamics of EEG microstates in four- to eight-year-old children: Age- and sex-related effects

The ultrafast spatiotemporal dynamics of large-scale neural networks can be examined using resting-state electroencephalography (EEG) microstates, representing transient periods of synchronized neural activity that evolve dynamically over time. In adults, four canonical microstates have been shown to explain most topographic variance in resting-state EEG. Their temporal structures are age-, sex- and state-dependent, and are susceptible to pathological brain states. However, no studies have assessed the spatial and temporal properties of EEG microstates exclusively during early childhood, a critical period of rapid brain development. Here we sought to investigate EEG microstates recorded with high-density EEG in a large sample of 103, 4-8-year-old children. Using data-driven k-means cluster analysis, we show that the four canonical microstates reported in adult populations already exist in early childhood. Using multiple linear regressions, we demonstrate that the temporal dynamics of two microstates are associated with age and sex. Source localization suggests that attention- and cognitive control-related networks govern the topographies of the age- and sex-dependent microstates. These novel findings provide unique insights into functional brain development in children captured with EEG microstates.

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. EEG-microstates (EEGms) are spatially featured states that can be regulated through NF training, given that they have recently been indicated as biomarkers for some disorders. The current study was conducted to develop an EEG-NF system for detecting “canonical 4 EEGms” in real time. There are four representative EEG states, regardless of the number of channels, preprocessing procedures, or participants. Accordingly, our 10 Hz NF system was implemented to detect them (msA, B, C, and D) and audio-visually inform participants of its detection. To validate the real-time effect of this system on participants’ performance, the NF was intentionally delayed for participants to prevent their cognitive control in learning. Our results suggest that the feedback effect was observed only under the no-delay condition. The number of Hits increased significantly from the baseline period and increased from the 1- or 20-s delay conditions. In addition, when the Hits were compared among the msABCD, each cognitive or perceptual function could be characterized, though the correspondence between each microstate and psychological ability might not be that simple. For example, msD should be generally task-positive and less affected by the inserted delay, whereas msC is more delay-sensitive. In this study, we developed and validated a new EEGms-NF system as a function of delay. Although the participants were naive to the inserted delay, the real-time NF successfully increased their Hit performance, even within a single-day experiment, although target specificity remains unclear. Future research should examine long-term training effects using this NF system.

Large-scale EEG neural network changes in response to therapeutic TMS

BACKGROUND

Transcranial magnetic stimulation (TMS) is an effective therapy for patients with treatment-resistant depression. TMS likely induces functional connectivity changes in aberrant circuits implicated in depression. Electroencephalography (EEG) "microstates" are topographies hypothesized to represent large-scale resting networks. Canonical microstates have recently been proposed as markers for major depressive disorder (MDD), but it is not known if or how they change following TMS.

METHODS

Resting EEG was obtained from 49 MDD patients at baseline and following six weeks of daily TMS. Polarity-insensitive modified k-means clustering was used to segment EEGs into constituent microstates. Microstates were localized via sLORETA. Repeated-measures mixed models tested for within-subject differences over time and t-tests compared microstate features between TMS responder and non-responder groups.

RESULTS

Six microstates (MS-1 - MS-6) were identified from all available EEG data. Clinical response to TMS was associated with increases in features of MS-2, along with decreased metrics of MS-3. Nonresponders showed no significant changes in any microstate. Change in occurrence and coverage of both MS-2 (increased) and MS-3 (decreased) correlated with symptom change magnitude over the course of TMS treatment.

CONCLUSIONS

We identified EEG microstates associated with clinical improvement following a course of TMS therapy. Results suggest selective modulation of resting networks observable by EEG, which is inexpensive and easily acquired in the clinic setting.

Dynamics of brain function in patients with chronic pain assessed by microstate analysis of resting-state electroencephalography

ABSTRACT

Chronic pain is a highly prevalent and severely disabling disease that is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of resting-state brain activity. However, brain activity varies over time, and it is increasingly recognized that the temporal dynamics of brain activity provide behaviorally relevant information in different neuropsychiatric disorders. Here, we therefore investigated whether the temporal dynamics of brain function are altered in chronic pain. To this end, we applied microstate analysis to eyes-open and eyes-closed resting-state electroencephalography data of 101 patients suffering from chronic pain and 88 age- and sex-matched healthy controls. Microstate analysis describes electroencephalography activity as a sequence of a limited number of topographies termed microstates that remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, consistently described resting-state brain activity in both groups in the eyes-closed condition. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in controls. As microstate D has previously been related to attentional networks and functions, these abnormalities might relate to dysfunctional attentional processes in chronic pain. Subgroup analyses replicated microstate D changes in patients with chronic back pain, while patients with chronic widespread pain did not show microstates alterations. Together, these findings add to the understanding of the pathophysiology of chronic pain and point to changes of brain dynamics specific to certain types of chronic pain.

EEG microstate analysis of emotion regulation reveals no sequential processing of valence and emotional arousal

In electroencephalography (EEG), microstates are distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Microstate analysis is a way of utilizing EEG as both temporal and spatial imaging tool, but has rarely been applied to task-based data. This study aimed to conceptually replicate microstate findings of valence and emotional arousal processing and investigate the effects of emotion regulation on microstates, using data of an EEG paradigm with 107 healthy adults who actively viewed emotional pictures, cognitively detached from them, or suppressed facial reactions. Within the first 600 ms after stimulus onset only the comparison of viewing positive and negative pictures yielded significant results, caused by different electrodes depending on the microstate. Since the microstates associated with more and less emotionally arousing pictures did not differ, sequential processing could not be replicated. When extending the analysis to 2000 ms after stimulus onset, differences were exclusive to the comparison of viewing and detaching from negative pictures. Intriguingly, we observed the novel phenomenon of a microstate difference that could not be attributed to single electrodes. This suggests that microstate analysis can detect differences beyond those detected by event-related potential analysis.

Dynamic task-linked switching between brain networks - A tri-network perspective

The highly influential tri-network model proposed by Menon integrates 3 key intrinsic brain networks - the central executive network (CEN), the salience network (SN), and the default mode network (DMN), into a single cohesive model underlying normal behaviour and cognition. A large body of evidence suggests that abnormal intra- and inter- network connectivity between these three networks underlies the various behavioural and cognitive dysfunctions observed in patients with neuropsychiatric conditions such as PTSD and depression. An important prediction of the tri-network model is that the DMN and CEN networks are anti-correlated under the control of the SN, such that if a task engages one of the two, the SN inhibits the activation of the other. To date most of the evidence surrounding the functions of these three core networks comes from either resting state analyses or in the context of a single task with respect to rest. Few studies have investigated multiple tasks simultaneously or characterized the dynamics of task switching. Hence, a careful investigation of the temporal dynamics of network activity during task switching is warranted. To accomplish this we collected fMRI data from 14 participants that dynamically switched between a 2-back working memory task and an autobiographical memory retrieval task, designed to activate the CEN, DMN and the SN. The fMRI data were used to 1. identify nodes and sub-networks within the three major networks involved in task-linked dynamic network switching, 2. characterize the temporal pattern of activation of these nodes and sub-networks, and finally 3. investigate the causal influence that these nodes and sub-networks exerted on each other. Using a combination of multivariate neuroimaging analyses, timecourse analyses and multivariate Granger causality measures to study the tri-network dynamics, the current study found that the SN co-activates with the task-relevant network, providing a mechanistic insight into SN-mediated network selection in the context of explicit tasks. Our findings also indicate active involvement of the posterior insula and some medial temporal nodes in task-linked functions of the SN and DMN, warranting their inclusion as network nodes in future studies of the tri-network model. These results add to the growing body of evidence showing the complex interplay of CEN, DMN and SN nodes and sub-networks required for adequate task-switching, characterizing a normative pattern of task-linked network dynamics within the context of Menon's tri-network model.

EEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations

Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency. To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave. In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis. We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm. These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.