Amir H. Omidvarnia

Functional Bimodality in the Brain Networks of Preterm and Term Human Newborns

The spontaneous brain activity exhibits long-range spatial correlations detected using functional magnetic resonance imaging (fMRI) signals in newborns when (1) long neuronal pathways are still developing, and (2) the electrical brain activity consists of developmentally unique, intermittent events believed to guide activity-dependent brain wiring. We studied this spontaneous electrical brain activity using multichannel electroencephalography (EEG) of premature and fullterm babies during sleep to assess the development of spatial integration during last months of gestation. Correlations of frequency-specific amplitudes were found to follow a robust bimodality: During low amplitudes (low mode), brain activity exhibited very weak spatial correlations. In contrast, the developmentally essential high-amplitude events (high mode) showed strong spatial correlations. There were no clear spatial patterns in the early preterm, but clear frontal and parieto-occipital modules at term age. A significant fronto-occipital gradient was also seen in the development of the graph measure clustering coefficient. Strikingly, no bimodality was found in the fMRI recordings of the fullterm babies, suggesting that early EEG activity and fMRI signal reflect different mechanisms of spatial coordination. The results are compatible with the idea that early developing human brain exhibits intermittent long-range spatial connections that likely provide the endogenous guidance for early activity-dependent development of brain networks.

Measuring Time-Varying Information Flow in Scalp EEG Signals: Orthogonalized Partial Directed Coherence

This study aimed to develop a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded electroencephalographic (EEG) signals in a way that is less affected by volume conduction and amplitude scaling. We modified the time-varying generalized partial directed coherence (tv-gPDC) method, by orthogonalization of the strictly causal multivariate autoregressive model coefficients, to minimize the effect of mutual sources. The novel measure, generalized orthogonalized PDC (gOPDC), was tested first using two simulated models with feature dimensions relevant to EEG activities. We then used the method for assessing event-related directional information flow from flash-evoked responses in neonatal EEG. For testing statistical significance of the findings, we followed a thresholding procedure driven by baseline periods in the same EEG activity. The results suggest that the gOPDC method 1) is able to remove common components akin to volume conduction effect in the scalp EEG, 2) handles the potential challenge with different amplitude scaling within multichannel signals, and 3) can detect directed information flow within a subsecond time scale in nonstationary multichannel EEG datasets. This method holds promise for estimating directed interactions between scalp EEG channels that are commonly affected by the confounding impact of mutual cortical sources.

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

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.

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

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 -10dB to 20dB. 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 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.

Kalman filter-based time-varying cortical connectivity analysis of newborn EEG

Multivariate Granger causality in the time-frequency domain as a representation of time-varying cortical connectivity in the brain has been investigated for the adult case. This is, however, not the case in newborns as the nature of the transient changes in the newborn EEG is different from that of adults. This paper aims to evaluate the performance of the time-varying versions of the two popular Granger causality measures, namely Partial Directed Coherence (PDC) and direct Directed Transfer Function (dDTF). The parameters of the time-varying AR, that models the inter-channel interactions, are estimated using Dual Extended Kalman Filter (DEKF) as it accounts for both non-stationarity and non-linearity behaviors of the EEG. Using simulated data, we show that fast changing cortical connectivity between channels can be measured more accurately using the time-varying PDC. The performance of the time-varying PDC is also tested on a neonatal EEG exhibiting seizure.

Structural damage in early preterm brain changes the electric resting state networks

A robust functional bimodality is found in the long-range spatial correlations of newborn cortical activity, and it likely provides the developmentally crucial functional coordination during the initial growth of brain networks. This study searched for possible acute effects on this large scale cortical coordination after acute structural brain lesion in early preterm infants. EEG recordings were obtained from preterm infants without (n=11) and with (n=6) haemorrhagic brain lesion detected in their routine ultrasound exam. The spatial cortical correlations in band-specific amplitudes were examined within two amplitude regimes, high and low amplitude periods, respectively. Technical validation of our analytical approach showed that bimodality of this kind is a genuine physiological characteristic of each brain network. It was not observed in datasets created from uniform noise, neither is it found between randomly paired signals. Hence, the observed bimodality arises from specific interactions between cortical regions. We found that significant long-range amplitude correlations are found in most signal pairs in both groups at high amplitudes, but the correlations are generally weaker in newborns with brain lesions. The group difference is larger during high mode, however the difference did not have any statistically apparent topology. Graph theoretical analysis confirmed a significantly larger weight dispersion in the newborns with brain lesion. Comparison of graph measures to a childs performance at two years showed that lower clustering coefficient and weight dispersion were both correlated to better neurodevelopmental outcomes. Our findings suggest that the common preterm brain haemorrhage causes diffuse changes in the functional long-range cortical correlations. It has been recently recognized that the high mode network activity is crucial for early brain development. The present observations may hence offer a mechanistic link between early lesion and the later emergence of complex neurocognitive sequelae.

Orthogonalized partial directed coherence for functional connectivity analysis of newborn EEG

The aim of this study is to develop a time-frequency method and test its applicability to investigating directional cortical connectivity in the newborn brain considering the effect of volume conduction. We modified time-varying partial directed coherence (tv-PDC) based on orthogonalization of the MVAR model coefficients to deal with the effect of mutual independent sources. The novel measure was then tested using a simulated signal with feature dimensions relevant to EEG activity. From the neonatal EEG responses evoked by flash light stimuli (1Hz), we extracted the directional interactions over time within each hemisphere. The results suggest that the method is able to detect directed information flow within a sub-second time scale in nonstationary multichannel signals (such as newborn EEG) and attenuate the problematic effect of volume conduction for multichannel EEG connectivity analysis.

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.

Generalized Mean Phase Coherence for asynchrony abnormality detection in multichannel newborn EEG

Inter-hemispheric asynchrony within the multichannel recordings of newborn EEG is associated with abnormal functionality of the newborn brain. Mean Phase Coherence (MPC) as a bivariate phase synchrony measure is widely used for pair-wise comparisons of scalp EEG phase information. A bivariate measure, however, is unlikely to capture the key feature of asynchrony seen in the sick neonatal brain, which is characterized by a global disruption of synchrony. In this study, the concept of cointegration is employed to generalize the bivariate MPC to deal with the multivariate case. The performance of the generalized MPC (GMPC) is evaluated using two simulated signals. It is also tested on a multichannel newborn EEG dataset with asynchronous inter-hemispheric bursts. The proposed method can be used to detect and quantify the degree of inter-hemispheric asynchrony from EEG signals.