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Background: TMS is being increasingly used as a noninvasive brain stimulation technique for the therapeutic management of partial epilepsies. However, the acute effects of TMS on epileptiform discharges (EDs, i.e. interictal epileptiform activity and subclinical electrographic seizure patterns) remain unexplored. Objective: To investigate whether TMS can modulate EDs in partial epilepsy. Methods: In Experiment Set 1, the safety of the TMS protocol was investigated in 10 well-controlled by anti-epileptic drugs (AEDs) epileptic patients. In Experiment Set 2, the effects of TMS on EDs were studied in three subjects with intractable frontal lobe epilepsies, characterized by particularly frequent EDs. TMS was applied over the electrographic focus with a circular and a figure of eight coil while recording EEG with a 60-channel TMS-compatible EEG system. The effectiveness of TMS in aborting EDs was investigated using survival analysis and brain connectivity analysis. Results: The TMS protocol was well-tolerated. TMS was an effective method to abort EDs even when adjusting for its latency with respect to ED onset (CMH test, p < 0.0001). While the effective brain connectivity around the epileptic focus increased significantly during EDs (p < 0.01), with TMS administration the increase was not statistically significant. Conclusion: TMS can modulate EDs in patients with epileptogenic foci in the cortical convexity and is associated with reversal of ED-induced changes in brain connectivity.

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

Background: Transcranial magnetic stimulation (TMS) can have inhibitory effects on epileptiform discharges (EDs) of patients with focal seizures. However, the brain connectivity before, during and after EDs, with or without the administration of TMS, has not been extensively explored. Objective: To investigate the brain network of effective connectivity during ED with and without TMS in patients with focal seizures. Methods: For the effective connectivity a direct causality measure is applied termed partial mutual information from mixed embedding (PMIME). TMS-EEG data from two patients with focal seizures were analyzed. Each EEG record contained a number of EDs in the majority of which TMS was administered over the epileptic focus. As a control condition, sham stimulation over the epileptogenic zone or real TMS at a distance from the epileptic focus was also performed. The change in brain connectivity structure was investigated from the causal networks formed at each sliding window. Conclusion: The PMIME could detect distinct changes in the network structure before, within, and after ED. The administration of real TMS over the epileptic focus, in contrast to sham stimulation, terminated the ED prematurely in a node-specific manner and regained the network structure as if it would have terminated spontaneously.

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework’s potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

The study of connectivity patterns of a system’s variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a better understanding of its internal evolutionary mechanisms. Here, the problem of estimating the connectivity network from multivariate time series in the presence of prominent unobserved variables is addressed. The causality measure of partial mutual information from mixed embedding (PMIME), designed to estimate direct lag-causal effects in the presence of many observed variables, is adapted to estimate also zero-lag effects, the so-called instantaneous causality. We term the proposed advanced method, PMIME0. The estimation of instantaneous causality by PMIME0 is a signature of the presence of hidden source in the observed system, as demonstrated analytically in a toy model. It is further demonstrated that the PMIME0 identifies the true instantaneous with great accuracy in a variety of high-dimensional dynamical systems. The method is applied to EEG data with epileptiform discharges (EDs), and the results imply a strong impact of unobserved confounders during the EDs. This finding comes as a possible explanation for the increased levels of causality during epileptic seizures estimated by some measures affected by the presence of a common source.

Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection models based on multiplex-multilayer network analysis (MMN) to explore functional network connectivity changes induced by robot-aided gait training (RAGT) using the Ekso, a wearable exoskeleton, and compared it to conventional overground gait training (COGT) in chronic stroke patients. We extracted the coreness of individual nodes at multiple locations in the brain from EEG recordings obtained before and after gait training in a resting state. We found that patients provided with RAGT achieved a greater motor function recovery than those receiving COGT. This difference in clinical outcome was paralleled by greater changes in connectivity patterns among different brain areas central to motor programming and execution, as well as a recruitment of other areas beyond the sensorimotor cortices and at multiple frequency ranges, contemporarily. The magnitude of these changes correlated with motor function recovery chances. Our data suggest that the use of RAGT as an add-on treatment to COGT may provide post-stroke patients with a greater modification of the functional brain network impairment following a stroke. This might have potential clinical implications if confirmed in large clinical trials.

Epilepsy is a neurological disorder caused by brain dysfunction, which could cause uncontrolled behavior, loss of consciousness and other hazards. Electroencephalography (EEG) is an indispensable auxiliary tool for clinical diagnosis. Great progress has been made by current seizure identification methods. However, the performance of the methods on different patients varies a lot. In order to deal with this problem, we propose an automatic seizure identification method based on brain connectivity learning. The connectivity of different brain regions is modeled by a graph. Different from the manually defined graph structure, our method can extract the optimal graph structure and EEG features in an end-to-end manner. Combined with the popular graph attention neural network (GAT), this method achieves high performance and stability on different patients from the CHB-MIT dataset. The average values of accuracy, sensitivity, specificity, F1-score and AUC of the proposed model are 98.90%, 98.33%, 98.48%, 97.72% and 98.54%, respectively. The standard deviations of the above five indicators are 0.0049, 0.0125, 0.0116 and 0.0094, respectively. Compared with the existing seizure identification methods, the stability of the proposed model is improved by 78–95%.

The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p < 0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p < 0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5–12.5 Hz), which is known to be the most responsive channel during resting state conditions.

To disclose the pathophysiological mechanism of schizophrenia from the general connectivity among distinct brain regions, the computational model proposed by Szalisznyó et al. is generalized by incorporating the effects of the hippocampus. Based on the center manifold theorem and normal form theory, the emergence of abnormal alpha oscillations, which are directly associated with symptoms of schizophrenia, is analyzed in this network with Hopf bifurcation as a precursor. It is found that the clinically significant hypoconnectivities between the anterior cingulate and the dorsolateral prefrontal cortex or from the dorsolateral prefrontal cortex to the hippocampus are helpful in inhibiting the abnormal alpha oscillations, while disconnection of the coupling from the anterior cingulate cortex to the striatum can induce schizophrenia. Moreover, polynomial chaos expansion and variance-based sensitivity analysis are applied to determine the contributions of these connection strengths to the variance of the neuronal population activity. Our model results are consistent with experimental observations and thus might be meaningful for clinical interference.

Connectivity analysis using modern approaches like Granger causality, partial directed coherence and transfer entropy always demands additional statistical evaluation of the obtained measures for significance. Although for very simple linear autoregressive processes and quasilinear oscillatory activities theoretical estimates are available, the real biological signals are too complex for application of analytical approaches and surrogate data come into use. When pathological activity like epileptic seizures is studied, the question can also rise in a somewhat different way: one asks whether the studied activity is different from the normal one rather than whether connectivity exists. The similar question is also valid if one compares connectivity in different physiological states like sleep and wakefulness.

Here, we study two different approaches to statistical evaluation of transfer entropy estimates in application to the study of spike–wave discharges (SWDs), the main encephalographic manifestation of absence epilepsy, registered in local field potentials of WAG/Rij rats (genetic models). The first approach is to compare distributions of the estimators for the baseline and different stages of pathological activity using traditional measures like t-test with additional corrections for multiple testing. The second approach is to make surrogate data and test whether the achieved estimators differ for surrogate series and for real ones. To support our findings and to understand the methods better, the series simulated using simple oscillatory models of epileptic activity are evaluated in the same way as the experimental data.

We show that the most pronounced phenomena like bidirectional increase in coupling between frontal and parietal cortical areas during SWDs in comparison to baseline activity are considered to be significant by both approaches. But when the less expressed coupling changes are under consideration, the approach base on surrogate data provides less false positives. These results confirm that the primary outcomes of connectivity analysis for absence epilepsy (and not only it) achieved previously are valid although the statistical evaluation of the connectivity estimators was suboptimal.