Imaginary Part Of Coherency Research Articles

Classification of pregnancy and labor contractions using a graph theory based analysis.

In this paper, we propose a new framework to characterize the electrohysterographic (EHG) signals recorded during pregnancy and labor. The approach is based on the analysis of the propagation of the uterine electrical activity. The processing pipeline includes i) the estimation of the statistical dependencies between the different recorded EHG signals, ii) the characterization of the obtained connectivity matrices using network measures and iii) the use of these measures in clinical application: the classification between pregnancy and labor. Due to its robustness to volume conductor, we used the imaginary part of coherence in order to produce the connectivity matrix which is then transformed into a graph. We evaluate the performance of several graph measures. We also compare the results with the parameter mostly used in the literature: the peak frequency combined with the propagation velocity (PV +PF). Our results show that the use of the network measures is a promising tool to classify labor and pregnancy contractions with a small superiority of the graph strength over PV+PF.

V3. Idiopathic generalized epilepsy shows widespread increased network connectivity

Introduction Idiopathic generalized epilepsy (IGE) is a subtype of epilepsy presumed to have a genetic etiology and characterized by generalized seizures, i.e. seizures starting and rapidly engaging distributed networks. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in idiopathic generalized epilepsy patients using diffusion tensor imaging and voxel based morphometry. Changes were also reported in functional networks during generalized spike wave discharges. In this study we wanted to investigate the IGE brain networks during the discharge-free intervals using Magnetoencephalography (MEG). To study the characteristics of the network, we used a graph theoretical approach. Methods We recorded resting state MEG data from 13 (9 females, mean age 38.6 years) IGE patients and 19 (11 females, mean age 38.5 years) healthy controls. Epileptic discharges were identified and marked by a neurologist and excluded from the analysis. Source localization of the resting state activity was performed using dynamic imaging of coherent sources (DICS) beamforming. Connectivity was estimated between all sources in the gray matter using the imaginary part of coherence. Another low-resolution network was generated by averaging connections between sources grouped by anatomical labeling. Graph measures were calculated on both the high resolution and low resolution networks. Results Network connectivity was significantly increased in IGE patients in the high-resolution networks in beta1 ( p = 0.003) and beta2 ( p = 0.0003) bands as compared to controls. In the low-resolution networks, connectivity in beta1 and beta2 was significantly higher in patients than in control subjects ( p = 0.005 and p = 0.0003 respectively). The further analysis of nodal strength using cluster-based statistics yielded significant clusters in the beta1 and beta2 bands. In the beta1 band, three significant clusters were found with higher nodal strength in IGE patients than in controls ( p = 0.004, p = 0.011 and p = 0.029 respectively). In the beta2 band, four significant clusters were found ( p = 0.0004, p = 0.002, p = 0.005 and p = 0.034 respectively) (see Fig. 1 ). We found no significant clusters of higher connectivity in controls compared to IGE patients in any of the frequency bands. Using Network Based Statistics, we found significant subnetworks with higher connectivity in IGE patients. The differences were found in alpha, beta1 and beta2 bands (see Fig. 2 ). We did not find any significant correlation between global connectivity and the age of participants. Conclusions Using graph theoretical network analysis, we found a widespread increase in connectivity in IGE patients compared to controls. These changes were more pronounced in the motor network and the mesio-frontal cortex. We did not, however, find any significant difference in clustering coefficient and characteristic path length which are measures that represent architectural differences in the network. Our findings suggest that an increased resting state connectivity in idiopathic generalized epilepsy could be a mechanism of seizure generation and represents a functional brain imaging endophenotype of the disease.

P177. Relation between cortical and subcortical neural dynamics on multiple time scales in patients with Parkinson’s disease

Long-range temporal correlations (LRTC) were frequently demonstrated in the cortically dominant alpha rhythm (8–13 Hz) in healthy subjects and in different disease states. In patients with Parkinson’s disease (PD), coherence was demonstrated in the dominant beta rhythms (approx. 10–30 Hz) in the basal ganglia. However, the relation between cortical LRTC (across tens of seconds) and subcortical coherence (milliseconds range) is unknown. We addressed these multi-scale interactions by simultaneous electroencephalographic (EEG) and local field potentials recordings from the bilateral subthalamic nuclei (STN) in eight patients undergoing deep brain stimulation for severe PD. In the on medications state, LRTC up to 20 s were calculated in the amplitude envelope of 8–13 Hz EEG oscillations (across whole scalp). The detectability of the imaginary part of coherency (iCOH) was calculated in 10–20 and 21–30 Hz oscillations in local field potential recordings from the STN. We demonstrated a significant positive correlation across patients between cortical LRTC (8–13 Hz) and subcortical iCOH (10–20 Hz) in the left STN. Furthermore, disease duration significantly correlated with the ratio between cortical LRTC (8–13 Hz) and subcortical iCOH (21–30 Hz) in the left STN. Our results demonstrate for the first time a relation between neural dynamics in the most dominant rhythms in cortex and basal ganglia across multiple time scales, which in addition was clinically relevant. The presence of these multi-scale dynamics contribute to our basic understanding of cortical-subcortical neural interactions.

Correlation between cortical and subcortical neural dynamics on multiple time scales in Parkinson’s disease

Complex amplitude dynamics of dominant alpha oscillations (8–13Hz) in the cortex can be captured with long-range temporal correlations (LRTC) in healthy subjects and in various diseases. In patients with Parkinson’s disease (PD), intra-nuclear coherence was demonstrated in dominant beta rhythms (10–30Hz) in the basal ganglia. However, so far the relation between cortical LRTC (across tens of seconds) and subcortical coherence (millisecond scale) is unknown. We addressed these “multiscale interactions” by simultaneous recordings of surface electroencephalography (EEG) and deep local field potentials (LFP) from the bilateral subthalamic nucleus (STN) in eight patients with severe PD eligible for deep brain stimulation, who performed a lexical decision task on medication. In the continuous data set LRTC up to 20s were calculated in the amplitude envelope of 8–13-Hz EEG oscillations (across whole scalp), and subcortical coherence was assessed with measures being insensitive to volume conduction artifacts (imaginary part of coherency; iCOH) in 10–20 and 21–30-Hz oscillations in STN–LFP. We showed a significant positive correlation across patients between cortical LRTC (8–13Hz) and subcortical iCOH selectively in 10–20-Hz oscillations in the left STN. Our results suggest a relation between neural dynamics in the most dominant rhythms in the cortex and basal ganglia in PD, extending across multiple time scales (milliseconds vs. tens of seconds). Furthermore, the investigation of multiscale interactions might contribute to our understanding of cortical–subcortical neural coupling in PD.

The influence of spontaneous brain oscillations on apparent motion perception

A good example of inferential processes in perception is long-range apparent motion (AM), the illusory percept of visual motion that occurs when two spatially distinct stationary visual objects are presented in alternating sequence. The AM illusion is strongest at presentation frequencies around 3Hz. At lower presentation frequencies, the percept varies from trial to trial between AM and sequential alternation, while at higher frequencies perception varies between AM and two simultaneously flickering objects. Previous studies have demonstrated that prestimulus alpha oscillations explain trial-to-trial variability in detection performance for visual stimuli presented at threshold. In the present study, we investigated whether fluctuations of prestimulus alpha oscillations can also account for variations in AM perception. Prestimulus alpha power was stronger when observers reported AM perception in subsequent trials with low presentation frequencies, while at high presentation frequencies there were no significant differences in alpha power preceding AM and veridical flicker perception. Moreover, when observers perceived AM the prestimulus functional connectivity between frontal and occipital channels was increased in the alpha band, as revealed by the imaginary part of coherency, which is insensitive to artefacts from volume conduction. Dynamic causal modelling of steady-state responses revealed that the most likely direction of this fronto-occipital connectivity was from frontal to occipital sources. These results point to a role of ongoing alpha oscillations in the inferential process that gives rise to the perception of AM and suggest that fronto-occipital interactions bias perception towards internally generated predictions.

Graph theoretical analysis indicates cognitive impairment in MS stems from neural disconnection.

BackgroundThe mechanisms underlying cognitive impairment in MS are still poorly understood. However, due to the specific pathology of MS, one can expect alterations in connectivity leading to physical and cognitive impairment.AimIn this study we aimed at assessing connectivity differences in EEG between cognitively impaired (CI) and cognitively preserved (CP) MS patients. We also investigated the influence of the measures used to construct networks.MethodsWe included 308 MS patients and divided them into two groups based on their cognitive score. Graph theoretical network analyses were conducted based on networks constructed using different connectivity measures, i.e. correlation, correlation in the frequency domain, coherence, partial correlation, the phase lag index and the imaginary part of coherency. The most commonly encountered network parameters were calculated and compared between the two groups using Wilcoxon's rank test. Clustering coefficients and path lengths were normalized to a randomized mean clustering coefficient and path length for each patient. False discovery rate was used to correct for the multiple comparisons and Cohen's d effect sizes are reported.ResultsCoherence analysis suggests that theta and delta connectivity is significantly smaller in cognitively impaired patients. Small-worldness differences are found in networks based on correlation, theta and delta coherence and correlation in the frequency domain. Modularity was related to age but not to cognition.ConclusionCognitive deterioration in MS is a symptom that seems to be caused by neural disconnections, probably the white matter tracts connecting both hemispheres, and leads to a wide range in network differences which can be assessed by applying GTA to EEG data. In the future, these results may lead to cheaper and more objective assessments of cognitive impairment in MS.

Instantaneous Granger Causality with the Hilbert-Huang Transform

Current measures of causality and temporal precedence have limited frequency and time resolution and therefore may not be viable in the detection of short periods of causality in specific frequencies. In addition, the presence of nonstationarities hinders the causality estimation of current techniques as they are based on Fourier transforms or autoregressive model estimation. In this work we present a combination of techniques to measure causality and temporal precedence between stationary and nonstationary time series, that is sensitive to frequency-specific short episodes of causality. This methodology provides a highly informative time-frequency representation of causality with existing causality measures. This is done by decomposing each time series into intrinsic oscillatory modes with an empirical mode decomposition algorithm and, subsequently, calculating their complex Hilbert spectrum. At each time point the cross-spectrum is calculated between time series and used to measure coherency and compute the transfer function and error covariance matrices using the Wilson-Burg method for spectral factorization. The imaginary part of coherency can then be computed as well as several Granger causality measures in the previous matrices. This work covers the most important theoretical background of these techniques and tries to prove the usefulness of this new approach while pointing out some of its qualities and drawbacks.

Brain Oscillations and Functional Connectivity during Overt Language Production

In the present study we investigate the communication of different large scale brain sites during an overt language production task with state of the art methods for the estimation of EEG functional connectivity. Participants performed a semantic blocking task in which objects were named in semantically homogeneous blocks of trials consisting of members of a semantic category (e.g., all objects are tools) or in heterogeneous blocks, consisting of unrelated objects. The classic pattern of slower naming times in the homogeneous relative to heterogeneous blocks is assumed to reflect the duration of lexical selection. For the collected data in the homogeneous and heterogeneous conditions the imaginary part of coherency (ImC) was evaluated at different frequencies. The ImC is a measure for detecting the coupling of different brain sites acting on sensor level. Most importantly, the ImC is robust to the artifact of volume conduction. We analyzed the ImC at all pairs of 56 EEG channels across all frequencies. Contrasting the two experimental conditions we found pronounced differences in the theta band at 7 Hz and estimated the most dominant underlying brain sources via a minimum norm inverse solution based on the ImC. As a result of the source localization, we observed connectivity between occipito-temporal and frontal areas, which are well-known to play a major role in lexical-semantic language processes. Our findings demonstrate the feasibility of investigating interactive brain activity during overt language production.

Methods to highlight consistency in repeated EEG recordings

In the present work, we aimed to find subject related features in EEG recordings which are consistent through multiple recordings and apply them in biometry. Essentially to use the brain's electroencephalographic activity as a possible way to identify individuals. Seventeen healthy subjects participated in the study and their brain activity were recorded through a 56 EEG channel, high-density EEG cap during one minute of resting state with eyes open and/or eyes closed. The subjects were participating in a second recording session as well, thus creating a dataset of ten closed and ten open eyed recordings each with follow-up measurements. Analyzing results of various testing scenarios involving power spectrum density (PSD), coherence (COH), and the imaginary part of coherence (iCOH) on segments of ten seconds, we concluded the best parameter setup as well as a minimal set of electrodes and the best possible feature vector assembly based on these computations. By using a naive Bayes classifier and K-fold cross-validations, we observed the highest correct recognition rates (CRR 98.33%) during eyes closed resting state at the parieto-occipital-temporal electrodes, suggesting these as the most stable characteristics therefore the most suitable, among those investigated here, for identifying individuals.

Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space

The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.

Characterization of motor and somatosensory function for stroke patients

In a pilot study, stroke patients with a lesion related to the motor system were studied using magnetoencephalography (MEG) and electromyography (EMG). The patients performed sustained finger movements for 30 s followed by 30 s of rest and 20 repetitions of this sequence in total. Task-related cortical signals derived from MEG were observed here at very different frequency scales. Slow signals below 0.1 Hz were extracted by independent component analysis and are associated with the sustained activation of the motor cortex, the dcMEG motor activation. MEG–EMG coupling phenomena in the 10–30 Hz range were analyzed using the imaginary part of coherency and are attributed to cortico-muscular coupling driving the muscles. Additionally a signal from the somatosensory cortex due to an electrical stimulation at the wrist, the N20m, was recorded as a physiological marker. Field maps and time series associated with the three types of signals are presented for one patient and one control subject as the signal quality of the patient data was not sufficient to achieve a group result. The feasibility of a comprehensive electrophysiological measuring and analysis procedure of the motor function for stroke research is demonstrated by the results.

Maximizing the imaginary part of coherency to detect cortico-cortical interactions

Event Abstract Back to Event Maximizing the imaginary part of coherency to detect cortico-cortical interactions Laura Marzetti1*, Stefania D. Penna1, Vittorio Pizzella1, Gian L. Romani1 and Guido Nolte2 1 G. D`Annunzio University Foundation, ITAB, Italy 2 Fraunhofer FIRST, Athena project team, Germany In order to understand brain function, it is fundamental to describe interactions between functionally specialized brain regions. To this end, MEG is an ideal technique thanks to its whole head coverage and temporal resolution that allow reconstruction of source activity at millisecond scale. Thus, neuronal interactions can be studied on the estimated brain source rather than at sensor level where severe limitations arise by volume conduction or field spread self-interaction effects[1]. Nevertheless, since this effect can never be completely abolished because unmixing of the sources bis never perfect, we estimate seed based cortico-cortical interaction by the imaginary part of coherency (IPC) which cannot be generated by independent sources [1]. When estimating brain sources without introducing anatomical constraints on dipole orientation, e.g. limiting sources to be normal to the cortex surface, brain signals along x, y and z spatial directions are obtained at each voxel using weighted minimum norm solutions. Principal component analysis (PCA) can be used to reduce dimensionality, but since PC is related to power an alternate approach can be used when analysing interaction. We propose an approach to find for each pair of voxels the respective dipole orientations which maximize the IPC between those voxels. The problem can be solved analytically in closed form. In this way we find for a given seed the largest possible interaction with any other voxel as measured by IPC. The method is tested on subjects performing a force controlled motor task (squeeze and release a water filled pump) with the left hand. As a seed region the contralateral primary motor area M1 is chosen and the IPC is estimated for various frequencies. Frequency specific preferential directions are found that correspond to patterns showing e.g. a somato-motor circuit involved in low alpha and a visuo-spatial attention network in high alpha.

Spatio-Temporal Analysis of Frontal Midline Theta Rhythm in EEG during 3-D Maze Task

We examined electroencephalography (EEG) power spectrum and complex coherence in the theta and alpha band while the subjects were performing a three - dimension (3-D) virtual maze navigation tasks. 10 healthy males participated. The imaginary part of coherency was used to provide a functional measurement of corticocortical communication. The imaginary part of coherency analysis applied to a measurement EEG data in the section which frontal midline theta rhythm (6 - 7 Hz) appeared during 3-D maze tasks. The results showed that the coherency in the theta band between regions of frontal and right temporal increased during 3-D maze tasks. This result suggests that the frontal midline theta rhythm modulate the component of the neural circuit, and greater connections between the cortex regions of the frontal - temporal increase during 3-D maze task. Therefore, this finding leads to conclusion that information processing in spatial navigation is related to regions of frontal and temporal.

Identifying true brain interaction from EEG data using the imaginary part of coherency

Objective: The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. Methods: We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. Results: We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2–4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. Conclusions: It is possible to reliably detect brain interaction during movement from EEG data. Significance: The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.