Multilayer network switching rate predicts brain performance

Large-scale brain dynamics are characterized by repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood, and whether switching between these states is important for behavior has been little studied. Our aim was to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved fMRI connectivity in 1,003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data were used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching, which we define as the rate at which each brain region transits between different networks. We found (i) an inverse relationship between network switching and connectivity dynamics, where the latter was defined in terms of time-resolved fMRI connections with variance in time that significantly exceeded phase-randomized surrogate data; (ii) brain connectivity was lower during intervals of network switching; (iii) brain areas with frequent network switching had greater temporal complexity; (iv) brain areas with high network switching were located in association cortices; and (v) using cross-validated elastic net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning, and amount of sleep. Our findings shed light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function

Robust estimation of highly-varying nonlinear instantaneous frequency of monocomponent signals using a lower-order complex-time distribution

This paper presents a robust method for estimating highly-changing nonlinear IFs in monocomponent signals using a lower order CTD based on the general concept of complex-time argument differentiation. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).This paper proposes an approach for robust estimation of highly-varying nonlinear instantaneous frequency (IF) in monocomponent nonstationary signals. The proposed method is based on a lower order complex-time distribution (CTD), derived by using the idea of complex-time differentiation of the instantaneous phase. Unlike other existing TFDs in the same framework, the proposed TFD is an order-free distribution which alleviates the subtractive cancellation error in IF estimation. The approach is applied to highly nonstationary monocomponent signals. Performance of the numerical implementation is compared with three existing IF estimation methods using three simulated signals. Noise analysis is also performed to evaluate the robustness of the method in presenfdece of additive noise at signal to noise ratio (SNR) varying from −10 dB to 20 dB. Results show that the proposed method outperforms the other methods at lower SNR and works reasonably well for the noiseless case

A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

This paper establishes the relevance of the GePS measure for quantifying the global phase synchronization within multivariate nonstationary signals such as newborn EEG using an IP/IF estimation approach in the time-frequency (T-F) domain. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).This paper proposes a new approach to estimate the phase synchrony among nonstationary multivariate signals using the linear relationships between their instantaneous frequency (IF) laws. For cases where nonstationary signals are multi-component, a decomposition method like multi-channel empirical mode decomposition (MEMD) is used to simultaneously decompose the multi-channel signals into their intrinsic mode functions (IMFs). We then apply the Johansen method on the IF laws to assess the phase synchrony within multivariate nonstationary signals. The proposed approach is validated first using multi-channel synthetic signals. The method is then used for quantifying the inter-hemispheric EEG asynchrony during ictal and inter-ictal periods using a newborn EEG seizure/non-seizure database of five subjects. For this application, pair-wise phase synchrony measures may not be able to account for phase interactions between multiple channels. Furthermore, the classical definition of phase synchrony, which is based on the rational relationships between phases, may not reveal the hidden phase interdependencies caused by irrational long-run relationships. We evaluate the performance of the proposed method using the differentiation of unwrapped phase as well as other IF estimation techniques. The results obtained on newborn EEG signals confirm that the generalized phase synchrony within EEG channels increases significantly during ictal periods. A statistically consistent phase coupling is also observed within the non-seizure segments supporting the concept of constant inter-hemispheric connectivity in the newborn brain during inter-ictal periods

Generalised phase synchrony within multivariate signals: An emerging concept in time-frequency analysis

This paper introduces the notion of the instantaneous frequency (IF) based generalized phase synchrony in time-frequency analysis based on the concept of cointegration. This phase synchrony is then quantified by investigating the linear relationships between IF laws of nonstationary multivariate signals. The proposed approach is applied to a multichannel newborn EEG signal and the results are compared with that of a bivariate phase synchrony measure.Scopu

Hierarchical disruption in the Bayesian brain: Focal epilepsy and brain networks

In this opinion paper, we describe a combined view of functional and effective brain connectivity along with the free-energy principle for investigating persistent disruptions in brain networks of patients with focal epilepsy. These changes are likely reflected in effective connectivity along the cortical hierarchy and construct the basis of increased local functional connectivity in focal epilepsy. We propose a testable framework based on dynamic causal modelling and functional connectivity analysis with the capacity of explaining commonly observed connectivity changes during interictal periods. We then hypothesise their possible relation with disrupted free-energy minimisation in the Bayesian brain. This may offer a new approach for neuroimaging to specifically develop and address hypotheses regarding the network pathomechanisms underlying epileptic phenotypes

Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach

This paper aims to assess and compare the performance of two brain connectivity measures based on time-varying multivariate AR modelling for newborn EEG analysis.Relationships between cortical neural recordings as a representation of functional connectivity between cortical brain regions were quantified using different time-frequency criteria. Among these, Partial Directed Coherence (PDC) and Directed Transfer Function (DTF) and their extensions have found wide acceptance. This paper aims to assess and compare the performance of these two connectivity measures that are based on time-varying multivariate AR modeling. The time-varying parameters of the AR model are estimated using an Adaptive AR modeling (AAR) approach and a short-time based stationary approach. The performance of these two approaches is compared using both simulated signal and a multichannel newborn EEG recording. The results show that the time-varying PDC outperforms the time-varying DTF measure. The results also point to the limitation of the AAR algorithm in tracking rapid parameter changes and the drawback of the short-time approach in providing high resolution time-frequency coherence functions. However, it can be demonstrated that time-varying MVAR representations of the cortical connectivity will potentially lead to better understanding of non-symmetric relations between EEG channels

Dynamic analysis of fMRI activation during epileptic spikes can help identify the seizure origin

Objective We use the dynamic electroencephalography-functional magnetic resonance imaging (EEG-fMRI) method to incorporate variability in the amplitude and field of the interictal epileptic discharges (IEDs) into the fMRI analysis. We ask whether IED variability analysis can (a) identify additional activated brain regions during the course of IEDs, not seen in standard analysis; and (b) demonstrate the origin and spread of epileptic activity. We explore whether these functional changes recapitulate the structural connections and propagation of epileptic activity during seizures. Methods Seventeen patients with focal epilepsy and at least 30 IEDs of a single type during simultaneous EEG-fMRI were studied. IED variability and EEG source imaging (ESI) analysis extracted time-varying dynamic changes. General linear modeling (GLM) generated static functional maps. Dynamic maps were compared to static functional maps. The dynamic sequence from IED variability was compared to the ESI results. In a subset of patients, we investigated structural connections between active brain regions using diffusion-based fiber tractography. Results IED variability distinguished the origin of epileptic activity from its propagation in 15 of 17 (88%) patients. This included two cases where no result was obtained from the standard GLM analysis. In both of these cases, IED variability revealed activation in line with the presumed epileptic focus. Two cases showed no result from either method. Both had very high spike rates associated with dysplasia in the postcentral gyrus. In all 15 cases with dynamic activation, the observed dynamics were concordant with ESI. Fiber tractography identified specific white matter pathways between brain regions that were active at IED onset and propagation. Significance Dynamic techniques involving IED variability can provide additional power for EEG-fMRI analysis, compared to standard analysis, revealing additional biologically plausible information in cases with no result from the standard analysis and gives insight into the origin and spread of IEDs

The dynamics of functional connectivity in neocortical focal epilepsy

Focal epilepsy is characterised by paroxysmal events, reflecting changes in underlying local brain networks. To capture brain network activity at the maximal temporal resolution of the acquired functional magnetic resonance imaging (fMRI) data, we have previously developed a novel analysis framework called Dynamic Regional Phase Synchrony (DRePS). DRePS measures instantaneous mean phase coherence within neighbourhoods of brain voxels. We use it here to examine how the dynamics of the functional connections of regional brain networks are altered in neocortical focal epilepsy. Using task-free fMRI data from 21 subjects with focal epilepsy and 21 healthy controls, we calculated the power spectral density of DRePS, which is a measure of signal variability in local connectivity estimates. Whole-brain averaged power spectral density of DRePS, or signal variability of local connectivity, was significantly higher in epilepsy subjects compared to healthy controls. Maximal increase in DRePS spectral power was seen in bilateral inferior frontal cortices, ipsilateral mid-cingulate gyrus, superior temporal gyrus, caudate head, and contralateral cerebellum. Our results provide further evidence of common brain abnormalities across people with focal epilepsy. We postulate that dynamic changes in specific cortical brain areas may help maintain brain function in the presence of pathological epileptiform network activity in neocortical focal epilepsy

Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection

Objective The process of manually marking up epileptic spikes for simultaneous electroencephalogram (EEG) and resting state functional MRI (rsfMRI) analysis in epilepsy studies is a tedious and subjective task for a human expert. The aim of this study was to evaluate whether automatic EEG spike detection can facilitate EEG-rsfMRI analysis, and to assess its potential as a clinical tool in epilepsy. Methods We implemented a fast algorithm for detection of uniform interictal epileptiform discharges (IEDs) in one-hour scalp EEG recordings of 19 refractory focal epilepsy datasets (from 16 patients) who underwent a simultaneous EEG-rsfMRI recording. Our method was based on matched filtering of an IED template (derived from human markup) used to automatically detect other ‘similar’ EEG events. We compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only. Results In contrast to human markup, automatic IED detection takes a much shorter time to detect IEDs and export an output text file containing spike timings. In 13/19 focal epilepsy datasets, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases automatic spike detection revealed additional brain regions not seen with human EEG markup. Additional events detected by our automated method independently revealed similar patterns of activation to a human markup. Overall, automatic IED detection provides greater statistical power in EEG-rsfMRI analysis compared to human markup in a short timeframe. Conclusions Automatic spike detection is a simple and fast method that can reproduce comparable and, in some cases, even superior results compared to the common practice of manual EEG markup in EEG-rsfMRI analysis of epilepsy. Significance Our study shows that IED detection algorithms can be effectively used in epilepsy clinical settings. This work further helps in translating EEG-rsfMRI research into a fast, reliable and easy-to-use clinical tool for epileptologists