Neural Connectivity in Epilepsy as Measured by Granger Causality

Dynamic modulations of effective brain connectivity associated with postural instability during multi-joint compound movement on compliant surface

Random fluctuations in somatosensory signals affect the ability of effectively coordinating multimodal information pertaining to the postural state during movement. Therefore, this study aimed to investigate the impact of a compliant surface on cortico-cortical causal information flow during multi-joint compound movements. Fifteen healthy adults (7 female / 8 male, 25.9 ± 4.0 years) performed 5 × 20 repetitions of bodyweight squats on firm and compliant surface. Motor behavior was quantified by center of pressure (CoP) displacements, hip movement and the root mean square of the rectus femoris activity. Using source space analysis, renormalized partial directed coherence (rPDC) computed subject-level multivariate effective brain connectivity of sensorimotor nodes. Bootstrap statistics revealed significantly decreased medio-lateral CoP displacement (p < 0.001), significantly increased velocity of medio-lateral hip motion (p < 0.001) as well as significantly lower rectus femoris activity (p < 0.01) in the compliant surface condition. On the cortical level, rPDC showed significantly modulated information flow in theta and beta frequencies for fronto-parietal edges (p < 0.01) only during the concentric phase of the movement. The compliant surface led to increased difficulties controlling hip but not center of pressure motion in the medio-lateral plane. Moreover, a decreased activation of the prime movers accompanied by modulations of effective brain connectivity among fronto-central nodes may point to altered demands on sensorimotor information processing in presence of sensory noise when performing bodyweight squats on compliant surface. Further studies are needed to evaluate a potential benefit for athletic and clinical populations.

Altered Directed-Connectivity Network in Temporal Lobe Epilepsy: A MEG Study

Temporal lobe epilepsy (TLE) is considered a network disorder rather than a localized lesion, making it essential to study the network mechanisms underlying TLE. In this study, we constructed directed brain networks based on clinical MEG data using the Granger Causality Analysis (GCA) method, aiming to provide new insights into the network mechanisms of TLE. MEG data from 13 lTLE and 21 rTLE patients and 14 healthy controls (HCs) were analyzed. The preprocessed MEG data were used to construct directed brain networks using the GCA method and undirected brain networks using the Pearson Correlation Coefficient (PCC) method. Graph theoretical analysis extracted global and local topologies from the binary matrix, and SVM classified topologies with significant differences (p < 0.05). Comparative studies were performed on connectivity strengths, graph theory metrics, and SVM classifications between GCA and PCC, with an additional analysis of GCA-weighted network connectivity. The results show that TLE patients showed significantly increased functional connectivity based on GCA compared to the control group; similarities of the hub brain regions between lTLE and rTLE patients and the cortical-limbic-thalamic-cortical loop were identified; TLE patients exhibited a significant increase in GCA-based Global Clustering Coefficient (GCC) and Global Local Efficiency (GLE); most brain regions with abnormal local topological properties in TLE patients overlapped with their hub regions. The directionality of brain connectivity has played a significantly more pivotal role in research on TLE. GCA may be a potential tool in MEG analysis to distinguish TLE patients and HC effectively.

Non-parametric full cross mapping (NFCM): a highly-stable measure for causal brain network and a pilot application

Objective.Measuring causal brain network from neurophysiological signals has recently attracted much attention in the field of neuroinformatics. Traditional data-driven algorithms are computationally time-consuming and unstable due to parameter settings.Approach.To resolve these limits, we proposed a novel parameter-free technique, called 'non-parametric full cross mapping (NFCM)'. The NFCM adapts current convergent cross-mapping concept, and makes two improvements: (1) an improved phase-space reconstruction with constant embedding parameters and (2) cross-mapping estimate of all embedding vectors on manifolds following simplex projection.Main results.Numerical experiments verify that our NFCM has the highest quantization stability even when perturbed by system noise, and its coefficient of variation is almost lower than that of the six baseline methods. The developed NFCM is finally used in stereoelectroencephalogram analysis of drug-resistant epilepsy in children (DREC). A total of 36 seizures, comprising 18 surgical successes and 18 failures, were included to explore the brain network dynamics. The average causal coupling in epileptogenic zones of successful surgery (0.81 ± 0.04) is significantly higher than that in non-epileptogenic zones (0.40 ± 0.03) withP<0.001via Mann-Whitney-U-test. While there is no significant difference among the 18 failed surgeries.Significance.The causal brain network measured by our NFCM is confirmed as a credible biomarker for localizing epileptogenic zones in DREC. These findings promise to advance precision medicine for DREC.

Enhancing non-invasive pre-surgical evaluation through functional connectivity and graph theory in drug-resistant focal epilepsy

BACKGROUND

Epilepsy, characterized as a network disorder, involves widely distributed areas following seizure propagation from a limited onset zone. Accurate delineation of the epileptogenic zone (EZ) is crucial for successful surgery in drug-resistant focal epilepsy. While visual analysis of scalp electroencephalogram (EEG) primarily elucidates seizure spreading patterns, we employed brain connectivity techniques and graph theory principles during the pre-ictal to ictal transition to define the epileptogenic network.

METHOD

Cortical sources were reconstructed from 40-channel scalp EEG in five patients during pre-surgical evaluation for focal drug-resistant epilepsy. Temporal Granger connectivity was estimated ten seconds before seizure and at seizure onset. Results have been analyzed using some centrality indices taken from Graph theory (Outdegree, Hubness). A new lateralization index is proposed by taking into account the sum of the most relevant hubness values across left and right regions of interest.

RESULTS

In three patients with positive surgical outcomes, analysis of the most relevant Hubness regions closely aligned with clinical hypotheses, demonstrating consistency in EZ lateralization and location. In one patient, the method provides unreliable results due to the abundant movement artifacts preceding the seizure. In a fifth patient with poor surgical outcome, the proposed method suggests a wider epileptic network compared with the clinically suspected EZ, providing intriguing new indications beyond those obtained with traditional electro-clinical analysis.

CONCLUSIONS

The proposed method could serve as an additional tool during pre-surgical non-invasive evaluation, complementing data obtained from EEG visual inspection. It represents a first step toward a more sophisticated analysis of seizure onset based on connectivity imbalances, electrical propagation, and graph theory principles.

Virtual resection evaluation based on sEEG propagation network for drug-resistant epilepsy

Drug-resistant epilepsy with frequent seizures are considered to undergo surgery to become seizure-free, but seizure-free rates have not dramatically improved, partly due to imprecise intervention locations. To address this clinical need, we construct effective connectivity to reveal epilepsy brain dynamics. Based on the propagation path captured by the high order effective connectivity, calculate the control centrality evaluation scheme of the excised area. We used three datasets: simulation dataset, clinical dataset, and public dataset. The epileptogenic propagation network was quantified by calculating high-order effective connection to obtain accurate propagation path, based on this, combined with the outdegree index for virtual resection. By removing electrodes and recalculating control centrality, we quantify each electrode or region's control centrality to evaluate the virtual resection scheme. Three datasets obtained consistent results. We track the accurate propagation path and find the obvious inflection points occurring during the excision process. The minimum intervention targets were obtained by comparing different schemes without recurrence. The clinical data with multiple seizures found that after resection, the brain reaches a stable state and is less likely to continue spreading. By quantitative analysis of control centrality to evaluate the possible excision scheme, finally we obtain the best intervention area for epilepsy, which assist in developing surgical plans.

Effective Connectivity Estimation by a Hybrid Neural Network, Empirical Wavelet Transform, and Bayesian Optimization

Accurately measuring nonlinear effective connectivity is a crucial step in investigating brain functions. Brain signals like EEG is nonstationary. Many effective connectivity methods have been proposed but they have drawbacks in their models such as a weakness in proposing a way for hyperparameter and time lag selection as well as dealing with non-stationarity of the time series. This paper proposes an effective connectivity model based on a hybrid neural network model which uses Empirical Wavelet Transform (EWT) and a long short-term memory network (LSTM). The best hyperparameters and time lag are selected using Bayesian Optimization (BO). Due to the importance of generalizability in neural networks and calculating GC, an algorithm was proposed to choose the best generalizable weights. The model was evaluated using simulated and real EEG data consisting of attention deficit hyperactivity disorder (ADHD) and healthy subjects. The proposed model's performance on simulated data was evaluated by comparing it with other neural networks, including LSTM, CNN-LSTM, GRU, RNN, and MLP, using a Blocked cross-validation approach. GC of the simulated data was compared with GRU, linear Granger causality (LGC), Kernel Granger Causality (KGC), Partial Directed Coherence (PDC), and Directed Transfer Function (DTF). Our results demonstrated that the proposed model was superior to the mentioned models. Another advantage of our model is robustness against noise. The results showed that the proposed model can identify the connections in noisy conditions. The comparison of the effective connectivity of ADHD and the healthy group showed that the results are in accordance with previous studies.

Causal Brain Network Predicts Surgical Outcomes in Patients With Drug-Resistant Epilepsy: A Retrospective Comparative Study

Network neuroscience, especially causal brain network, has facilitated drug-resistant epilepsy (DRE) studies, while surgical success rate in patients with DRE is still limited, varying from 30%  ∼  70 %. Predicting surgical outcomes can provide additional guidance to adjust treatment plans in time for poorly predicted curative effects. In this retrospective study, we aim to systematically explore biomarkers for surgical outcomes by causal brain network methods and multicenter datasets. Electrocorticogram (ECoG) recordings from 17 DRE patients with 58 seizures were included. Ictal ECoG within clinically annotated epileptogenic zone (EZ) and non-epileptogenic zone (NEZ) were separately computed using six different algorithms to construct causal brain networks. All the brain network results were divided into two groups, successful and failed surgeries. Statistical results based on the Mann-Whitney-U-test show that: causal connectivity of α -frequency band ( 8  ∼  13 Hz) in EZ calculated by convergent cross mapping (CCM) gains the most significant differences between the surgical success and failure groups, with a P value of 7.85e-08 and Cohen's d effect size of 0.77. CCM-defined EZ brain network can also distinguish the successful and failed surgeries considering clinical covariates (clinical centers, DRE types) with [Formula: see text]. Based on the brain network features, machine learning models were developed to predict the surgical outcomes. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest average accuracy of 84.48% by 5-fold cross-validation, further indicating that the CCM-defined EZ brain network is a reliable biomarker for predicting DRE surgical outcomes.

Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment.

METHODS

We analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters.

RESULTS

Both AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD.

CONCLUSIONS

Our findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.

A Stable EEG Epilepsy Network Spans From Infraslow to Ripple and From Interictal to Ictus

PURPOSE

To characterize the epilepsy network as reflected in intracranial electroencephalography (iEEG) across the full spectrum of iEEG frequencies and different phases of epilepsy, using a single, conceptually straightforward mathematical measure.

METHODS

The authors applied the spectral Granger causality techniques to intracranial electroencephalography recordings and computed contact-by-contact inward, outward, and total causal flow across frequencies and seizure phases in a selected group of three patients with well-defined, nonlesional seizure foci and prolonged responses to invasive procedures. One seizure and one interictal sample were analyzed per subject.

RESULTS

A prominent intracranial electroencephalography network was identified by Granger causality at both high and low frequencies. This network persists during the preictal and interictal phases of epilepsy and closely matches the visible seizure onset. The causal inflow network corresponded to seizure onset electrode contacts in 8 of 12 conditions, including ripple, infraslow, preictal, and interictal phases of epilepsy. Its most striking feature is the consistent dominance of causal inflow rather than outflow in the vicinity of the seizure onset zone.

CONCLUSIONS

Findings of this study indicate that a stable intracranial electroencephalography epilepsy network persists, and it can be characterized by a single Granger causality measure from infraslow to ripple frequencies and from the interictal to the immediate preictal phases of epilepsy.

The applied principles of EEG analysis methods in neuroscience and clinical neurology

Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy

Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.

Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model

Neuromodulation has emerged as a promising technique for the treatment of epilepsy. The target for neuromodulation is critical for the effectiveness of seizure control. About 30% of patients with drug-resistant epilepsy (DRE) fail to achieve seizure freedom after surgical intervention. It is difficult to find effective brain targets for neuromodulation in these patients because brain regions are damaged during surgery. In this study, we propose a novel approach for localizing neuromodulatory targets, which uses intracranial EEG and multi-unit computational models to simulate the dynamic behavior of epileptic networks through external stimulation. First, we validate our method on a multivariate autoregressive model and compare nine different methods of constructing brain networks. Our results show that the directed transfer function with surrogate analysis achieves the best performance. Intracranial EEGs of 11 DRE patients are further analyzed. These patients all underwent surgery. In three seizure-free patients, the localized targets are concordant with the resected regions. For the eight patients without seizure-free outcome, the localized targets in three of them are outside the resected regions. Finally, we provide candidate targets for neuromodulation in these patients without seizure-free outcome based on virtual resected epileptic network. We demonstrate the ability of our approach to locate optimal targets for neuromodulation. We hope that our approach can provide a new tool for localizing patient-specific targets for neuromodulation therapy in DRE.

Brain and brain-heart Granger causality during wakefulness and sleep

In this exploratory study we apply Granger Causality (GC) to investigate the brain-brain and brain-heart interactions during wakefulness and sleep. Our analysis includes electroencephalogram (EEG) and electrocardiogram (ECG) data during all-night polysomnographic recordings from volunteers with apnea, available from the Massachusetts General Hospital's Computational Clinical Neurophysiology Laboratory and the Clinical Data Animation Laboratory. The data is manually annotated by clinical staff at the MGH in 30 second contiguous intervals (wakefulness and sleep stages 1, 2, 3, and rapid eye movement (REM). We applied GC to 4-s non-overlapping segments of available EEG and ECG across all-night recordings of 50 randomly chosen patients. To identify differences in GC between the different sleep stages, the GC for each sleep stage was subtracted from the GC during wakefulness. Positive (negative) differences indicated that GC was greater (lower) during wakefulness compared to the specific sleep stage. The application of GC to study brain-brain and brain-heart bidirectional connections during wakefulness and sleep confirmed the importance of fronto-posterior connectivity during these two states, but has also revealed differences in ipsilateral and contralateral mechanisms of these connections. It has also confirmed the existence of bidirectional brain-heart connections that are more prominent in the direction from brain to heart. Our exploratory study has shown that GC can be successfully applied to sleep data analysis and captures the varying physiological mechanisms that are related to wakefulness and different sleep stages.

Ictal neural oscillatory alterations precede sudden unexpected death in epilepsy

Sudden unexpected death in epilepsy is the most catastrophic outcome of epilepsy. Each year there are as many as 1.65 cases of such death for every 1000 individuals with epilepsy. Currently, there are no methods to predict or prevent this tragic event, due in part to a poor understanding of the pathologic cascade that leads to death following seizures. We recently identified enhanced seizure-induced mortality in four inbred strains from the genetically diverse Collaborative Cross mouse population. These mouse models of sudden unexpected death in epilepsy provide a unique tool to systematically examine the physiological alterations during fatal seizures, which can be studied in a controlled environment and with consideration of genetic complexity. Here, we monitored the brain oscillations and heart functions before, during, and after non-fatal and fatal seizures using a flurothyl-induced seizure model in freely moving mice. Compared with mice that survived seizures, non-survivors exhibited significant suppression of brainstem neural oscillations that coincided with cortical epileptic activities and tachycardia during the ictal phase of a fatal seizure. Non-survivors also exhibited suppressed delta (0.5-4 Hz)/gamma (30-200 Hz) phase-amplitude coupling in cortex but not in brainstem. A connectivity analysis revealed elevated synchronization of cortex and brainstem oscillations in the delta band during fatal seizures compared with non-fatal seizures. The dynamic ictal oscillatory and connectivity features of fatal seizures provide insights into sudden unexpected death in epilepsy and may suggest biomarkers and eventual therapeutic targets.

Emotion depends on context, culture and their interaction: evidence from effective connectivity

Situated models of emotion hypothesize that emotions are optimized for the context at hand, but most neuroimaging approaches ignore context. For the first time, we applied Granger causality (GC) analysis to determine how an emotion is affected by a person's cultural background and situation. Electroencephalographic recordings were obtained from mainland Chinese (CHN) and US participants as they viewed and rated fearful and neutral images displaying either social or non-social contexts. Independent component analysis and GC analysis were applied to determine the epoch of peak effect for each condition and to identify sources and sinks among brain regions of interest. We found that source-sink couplings differed across culture, situation and culture × situation. Mainland CHN participants alone showed preference for an early-onset source-sink pairing with the supramarginal gyrus as a causal source, suggesting that, relative to US participants, CHN participants more strongly prioritized a scene's social aspects in their response to fearful scenes. Our findings suggest that the neural representation of fear indeed varies according to both culture and situation and their interaction in ways that are consistent with norms instilled by cultural background.

Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia

Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.

Combination of Matching Responsive Stimulations of Hippocampus and Subiculum for Effective Seizure Suppression in Temporal Lobe Epilepsy

Responsive neural stimulation (RNS) is considered a promising neural modulation therapy for refractory epilepsy. Combined stimulation on different targets may hold great promise for improving the efficacy of seizure control since neural activity changed dynamically within associated brain targets in the epileptic network. Three major issues need to be further explored to achieve better efficacy of combined stimulation: (1) which nodes within the epileptogenic network should be chosen as stimulation targets? (2) What stimulus frequency should be delivered to different targets? and (3) Could the efficacy of RNS for seizure control be optimized by combined different stimulation targets together? In our current study, Granger causality (GC) method was applied to analyze epileptogenic networks for finding key targets of RNS. Single target stimulation (100 μA amplitude, 300 μs pulse width, 5s duration, biphasic, charge-balanced) with high frequency (130 Hz, HFS) or low frequency (5 Hz, LFS) was firstly delivered by our lab designed RNS systems to CA3, CA1, subiculum (SUB) of hippocampi, and anterior nucleus of thalamus (ANT). The efficacy of combined stimulation with different groups of frequencies was finally assessed to find out better combined key targets with optimal stimulus frequency. Our results showed that stimulation individually delivered to SUB and CA1 could shorten the average duration of seizures. Different stimulation frequencies impacted the efficacy of seizure control, as HFS delivered to CA1 and LFS delivered to SUB, respectively, were more effective for shortening the average duration of electrographic seizure in Sprague-Dawley rats (n = 3). Moreover, the synchronous stimulation of HFS in CA1 combined with LFS in SUB reduced the duration of discharge significantly in rats (n = 6). The combination of responsive stimulation at different targets may be an inspiration to optimize stimulation therapy for epilepsy.

Effective connectivity alteration according to recurrence in transient global amnesia

PURPOSE

This study aimed to evaluate alterations in structural covariance network and effective connectivity of the intrahippocampal circuit in patients with transient global amnesia (TGA). We also investigated whether there were differences of them according to recurrence.

METHODS

We enrolled 88 patients with TGA and 50 healthy controls. We classified patients with TGA into two groups: the single event group (N = 77) and recurrent events group (N = 11). We performed volumetric analysis using the FreeSurfer program and structural covariance network analysis based on the structural volumes using a graph theoretical analysis in patients with TGA and healthy controls. The effective connectivity of the intrahippocampal circuit was also evaluated using structural equation modeling.

RESULTS

There were no significant differences between patients with all TGA events/a single TGA event and healthy controls with regard to global structural covariance network. However, patients with recurrent events had significant alterations in global structural covariance network with a decrease in the small-worldness index (0.907 vs. 0.970, p = 0.032). In patients with all events/a single, there were alterations in effective connectivity from the entorhinal cortex to CA4, only. However, in patients with recurrent events, there were alterations in effective connectivity from the subiculum to the fimbria as well as from the entorhinal cortex to CA4 in bilateral hemispheres.

CONCLUSION

Our study revealed significant alterations in structural covariance network and disruption of the intrahippocampal circuit in patients with TGA compared to healthy controls, which is more prominent when amnestic events recurred. It could be related to the pathogenesis of TGA.

Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.

Time-variant Epileptic Brain Functional Connectivity of Focal and Generalized Seizure in Chronic Temporal Lobe Epilepsy Rat. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:2833-2836. [PMID: 33018596 DOI: 10.1109/embc44109.2020.9175924]

Seizure types and characteristics may vary with time in a patient with distinct mechanisms underlying the propagation of ictal activity. Similarly, we found that both focal and generalized seizures coexist in some pilocarpine-induced chronic temporal lobe epilepsy (TLE) rats. In different seizure patterns, mapping complex networks and analyzing epileptic characteristics involved in seizure propagation are likely to reflect seizure propagation mechanisms, and indicate the establishment of stimulation strategy for epilepsy treatment, especially on the selection of stimulation targets. In our study, we used Granger causality method to track the time-variant epileptic brain functional connectivity in focal and generalized seizures from multi-site local field potentials (LFPs). Results showed that these two major types of seizures had different propagation patterns during ictal period. When comparing them, generalized seizures involved in a network with more complex relationships and spread to more extensive brain regions than in local seizures at mid-ictal stage. Moreover, we observed that focal seizures had a focused causal hub with strong interactions, while generalized seizures had relative distributed causal hubs to drive the development of seizure during seizure-onset stage. These findings suggest that stimulation strategy might need to be adapted to different seizure types thus allowing for retuning abnormal epileptic brain network and obtaining better treatment effect on seizure suppression.

Complex network analysis of MCI-AD EEG signals under cognitive and resting state

OBJECTIVE

The purpose of this study is to characterize functional connectivity changes in mild cognitive impaired Alzheimer's disease (MCI-AD) under resting and cognitive task conditions.

METHOD

EEG signals were recorded under resting states (Eyes closed (EC) and Eyes open (EO)) and cognitive states (Mental Arithmetic Eyes closed (MAEC) and Mental Arithmetic Eyes open (MAEO)) conditions. Functional connectivity metrics were calculated using weighted phase lag index (WPLI). Topological features of the functional connectivity network were analyzed through minimum spanning tree (MST) algorithm. Betweenness centrality was estimated in five different regions of the brain to study hub importance.

RESULTS

An increase in values of eccentricity and diameter were observed in patient group in five frequency bands of delta, theta, alpha1, alpha 2 and beta bands under resting and cognitive states. A reduction in leaf fraction was observed in alpha 1 band of EO condition. The results indicated a reduction in functional integration in the brain network of MCI-AD patients. Analysis of MST parameters revealed a higher disintegrated network for the alpha band under EO protocol. The study of hub status in the network displayed an elevated hub status in the central region for the patient group under cognitive task. The study also observed increased integration in gamma band in MCI - AD subjects than healthy controls under cognitive load.

CONCLUSION

Disintegration of functional network in alpha band under eyes open protocol and elevated hub strength in central region during cognitive task could be distinguishing features that could aid early detection of AD.

Dynamic Neuromagnetic Network Changes of Seizure Termination in Absence Epilepsy: A Magnetoencephalography Study

Objective: With increasing efforts devoted to investigating the generation and propagation mechanisms of spontaneous spike and wave discharges (SWDs), little attention has been paid to network mechanisms associated with termination patterns of SWDs to date. In the current study, we aimed to identify the frequency-dependent neural network dynamics during the offset of absence seizures.

Methods: Fifteen drug-naïve patients with childhood absence epilepsy (CAE) were assessed with a 275-Channel Magnetoencephalography (MEG) system. MEG data were recorded during and between seizures at a sampling rate of 6,000 Hz and analyzed in seven frequency bands. Source localization was performed with accumulated source imaging. Granger causality analysis was used to evaluate effective connectivity networks of the entire brain at the source level.

Results: At the low-frequency (1–80 Hz) bands, activities were predominantly distributed in the frontal cortical and parieto–occipito–temporal junction at the offset transition periods. The high-frequency oscillations (HFOs, 80–500 Hz) analysis indicated significant source localization in the medial frontal cortex and deep brain areas (mainly thalamus) during both the termination transition and interictal periods. Furthermore, an enhanced positive cortico–thalamic effective connectivity was observed around the discharge offset at all of the seven analyzed bands, the direction of which was primarily from various cortical regions to the thalamus.

Conclusions: Seizure termination is a gradual process that involves both the cortices and the thalamus in CAE. Cortico–thalamic coupling is observed at the termination transition periods, and the cerebral cortex acts as the driving force.

Ictal High-Frequency Oscillation for Lateralizing Patients With Suspected Bitemporal Epilepsy Using Wavelet Transform and Granger Causality Analysis

Identifying lateralization of bilateral temporal lobe epilepsy (TLE) is a challenging issue; scalp electroencephalography (EEG) and routine band electrocorticography (ECoG) fail to reveal the epileptogenic focus for further temporal lobectomy treatment. High-frequency oscillations (HFOs) can be utilized as a biomarker for lateralizing the onset zone in suspected bitemporal epilepsy. Except subjective vision detect the HFOs, objective verification should be performed to raise the accuracy. In the present research, we prospectively studied 10 patients with refractory temporal seizures and who underwent ECoG with wide-band frequency amplifiers (2,048 Hz); all patients had a class I outcome after temporal resection. Pre- and ictal HFOs will be analyzed by wavelet transform (WT) and Granger causality (GC) to objectively verify lateralization of the seizure onset zone (SOZ). WT analysis showed ictal HFOs in 10 patients mainly covered from 80 to 115 Hz (average, 92.59 ± 10.23 Hz), and there was distinct bandpass boundary between pre-ictal HFOs and ictal HFOs. GC analysis showed five patients (2, 4, 5, 6, and 7), no matter the pre-ictal or ictal state, had the highest GC degree in SOZ itself. The remaining patients (1, 3, 8, 9, and 10) had the highest GC degree in SOZ with its adjacent regions in the pre-ictal and ictal stages. GC analysis further confirmed the result of the WT and suggested HFOs are initiated and propagated in the local brain region mainly, afterward, transmitting to adjacent brain regions. These results indicated that the combination of WT and GC analyses significantly contributes to accurate lateralization in patients with suspected bitemporal epilepsy.

Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods

Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.

Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality

The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

Neural Correlates of Workload Transition in Multitasking: An ACT-R Model of Hysteresis Effect

This study investigated the effect of task demand transitions at multiple levels of analysis including behavioral performance, subjective rating, and brain effective connectivity, while comparing human data to Adaptive Control of Thought-Rational (ACT-R) simulated data. Three stages of task demand were designed and performed sequentially (Low-High-Low) during AF-MATB tasks, and the differences in neural connectivity during workload transition were identified. The NASA Task Load Index (NASA-TLX) and the Instantaneous Self-Assessment (ISA) were used to measure the subjective mental workload that accompanies the hysteresis effect in the task demand transitions. The results found significant hysteresis effects on performance and various brain network measures such as outflow of the prefrontal cortex and connectivity magnitude. These findings would assist in clarifying the direction and strength of the Granger Causality under demand transitions. As a result, these findings involving the neural mechanisms of hysteresis effects in multitasking environments may be utilized in applications of neuroergonomics research. The ability to compare data derived from human participants to data gathered by the ACT-R model allows researchers to better account for hysteresis effects in neuro-cognitive models in the future.

Altered attention networks and DMN in refractory epilepsy: A resting-state functional and causal connectivity study

PURPOSE

Epilepsy is considered a disorder of neural networks. Patients diagnosed with refractory epilepsy frequently experience attention impairments. Seizure activity in epilepsy may disturb brain networks and damage the brain function of attention. The aims of this study were to assess functional and causal connectivities of the attention networks and default mode network using resting-state functional magnetic resonance imaging (fMRI).

METHOD

Resting-state fMRI data were gathered from 19 patients with refractory epilepsy (mixed localization and aetiologies) and 21 healthy people. The fMRI data were analyzed by group independent component analysis (ICA) fMRI toolbox to extract dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN). The components of the selected networks were compared between patients and healthy controls to explore the change in functional connectivity (FC). Granger causality analysis was performed by taking the aforementioned significant brain areas as regions of interest (ROIs) to calculate autoregression coefficients of each pair of ROIs. Comparisons were done to find the significantly different causal connectivity when FC was changed between patients and healthy controls.

RESULTS

In DAN, the FC values of the bilateral frontal eye field (FEF) and left intraparietal sulcus (IPS) were decreased. In VAN, the FC values of the double-side ventral prefrontal cortex (vPFC) and the temporoparietal junction (TPJ) were reduced. As for DMN, the FC values of the bilateral medial prefrontal cortices (mPFC) were decreased whereas those for the bilateral precuneus (PCUN) were increased. Granger causal connectivity values were correlated: causal influence was decreased significantly from the left IPS (in DAN) to the double side of the vPFC but remained the same for the right FEF (in DAN) to the right TPJ. The value was decreased from the left PCUN (in DMN) to the right TPJ and FEF, and the causal flow from the right PCUN to the right TPJ and bilateral vPFC was also significantly inhibited (p < 0.05).

CONCLUSION

Frequent seizures in patients with refractory epilepsy may damage the cortex and disturb DAN, VAN, and DMN, leading to functional and causal connectivity alteration. In addition, epileptic activity may disrupt network interactions and further influence information communication.

Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network

OBJECTIVE

Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for directional connection analysis in multivariate time series. However, most existing methods based on Granger causality assume fixed time lags in information transmission, while the propagation delay between brain signals is usually changing constantly.

METHODS

We propose a Granger causality estimator based on the recurrent neural network, called RNN-GC, to deal with the multivariate brain connectivity detection problem. Our model takes input of time-series signals with arbitrary length of transmission time lags and learns the information flow from the data using the gated RNN model, i.e., long short-term memory (LSTM). The LSTM model can sequentially update the gates in memory cells to determine how many preceding points should be considered for prediction. Therefore, the LSTM-based RNN-GC estimator works well on varying-length time lags and shows effectiveness even on very long transmission delays.

RESULTS

Experiments are carried out in comparison with other methods using both simulation data and epileptic electroencephalography signals. The RNN-GC estimator achieves superior performance in brain connectivity estimation and shows robustness in modeling multivariate connections with varying-length time lags.

CONCLUSION

The RNN-GC method is capable of modeling nonlinear and varying-length lagged information transmission and effective in directional brain connectivity estimation.

SIGNIFICANCE

The proposed method is promising to serve as a robust brain connection analysis tool in clinical applications.

Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis

Epilepsy is a brain disorder characterised by the recurrent and unpredictable interruptions of normal brain function, called epileptic seizures. The present study attempts to derive new diagnostic indices which may delineate between ictal and interictal states of epilepsy. To achieve this, the nonlinear modeling approach of global principal dynamic modes (PDMs) is adopted to examine the functional connectivity of the temporal and frontal lobes with the occipital brain segment using an ensemble of paediatric EEGs having the presence of epileptic seizure. The distinct spectral characteristics of global PDMs are found to be in line with the neural rhythms of brain dynamics. Moreover, we find that the linear trends of associated nonlinear functions (ANFs) associated with the 2nd and 4th global PDMs (representing delta, theta and alpha bands) of Fp1–F3 may differentiate between ictal and interictal states of epilepsy. These findings suggest that global PDMs and their associated ANFs may offer potential utility as diagnostic neural measures for ictal and interictal states of epilepsy.

Multimodal investigation of epileptic networks: The case of insular cortex epilepsy

The insula is a deep cortical structure sharing extensive synaptic connections with a variety of brain regions, including several frontal, temporal, and parietal structures. The identification of the insular connectivity network is obviously valuable for understanding a number of cognitive processes, but also for understanding epilepsy since insular seizures involve a number of remote brain regions. Ultimately, knowledge of the structure and causal relationships within the epileptic networks associated with insular cortex epilepsy can offer deeper insights into this relatively neglected type of epilepsy enabling the refining of the clinical approach in managing patients affected by it. In the present chapter, we first review the multimodal noninvasive tests performed during the presurgical evaluation of epileptic patients with drug refractory focal epilepsy, with particular emphasis on their value for the detection of insular cortex epilepsy. Second, we review the emerging multimodal investigation techniques in the field of epilepsy, that aim to (1) enhance the detection of insular cortex epilepsy and (2) unveil the architecture and causal relationships within epileptic networks. We summarize the results of these approaches with emphasis on the specific case of insular cortex epilepsy.

Behavioral and Neural Correlates of Executive Function: Interplay between Inhibition and Updating Processes

This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity. We found that both behavioral (accuracy and reaction time) and neurophysiological (P300 amplitude and alpha band power) metrics on the inhibition task (i.e., flanker task) were influenced by the updating load (n-back level) and modulated by working memory capacity. Using independent component analysis, source localization (DIPFIT), and Granger Causality analysis of the EEG time-series data, the present study demonstrated that manipulation of cognitive demand in a dual executive function task influenced the causal neural network. We compared connectivity across three updating loads (n-back levels) and found that experimental manipulation of working memory load enhanced causal connectivity of a large-scale neurocognitive network. This network contains the prefrontal and parietal cortices, which are associated with inhibition and updating executive function processes. This study has potential applications in human performance modeling and assessment of mental workload, such as the design of training materials and interfaces for those performing complex multitasking under stress.

Effective connectivity in temporal lobe epilepsy with hippocampal sclerosis

OBJECTIVE

We hypothesized that temporal lobe epilepsy (TLE) patients with and without hippocampal sclerosis (HS) showed differences in their limbic networks. This study aimed to evaluate the role of the thalamus in TLE patients with HS.

MATERIALS AND METHODS

Twenty-nine TLE patients with HS and 30 controls were enrolled in this study. In addition, we included eight TLE patients without HS as a disease control group. Using whole-brain T1-weighted MRIs, we analyzed the volumes of the limbic structures, including the hippocampus, thalamus, and total cortex, with FreeSurfer 5.1. We also investigated the effective connectivity among these structures using SPSS Amos 21 based on these volumetric measures. Moreover, we quantified correlations between epilepsy duration and the volumes of these structures.

RESULTS

There was a statistically significant effective connectivity from the hippocampus to the thalamus in TLE patients with HS. Moreover, the volumes of the left and right thalamus were negatively correlated with epilepsy duration (r=-.42, P=.0315 and r=-.52, P=.0062, respectively). However, neither TLE patients without HS nor normal controls had a significant effective connectivity from the hippocampus to the thalamus.

CONCLUSIONS

The limbic networks of TLE patients with and without HS could be different, and the thalamus might play a critical role in TLE patients with HS.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

OBJECTIVE

Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions.

METHODS

Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG).

RESULTS

Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection.

CONCLUSION

Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity).

SIGNIFICANCE

The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

The Influence of Solid Microneedles on the Transdermal Delivery of Selected Antiepileptic Drugs

The aim of this project was to examine the effect of microneedle rollers on the percutaneous penetration of tiagabine hydrochloride and carbamazepine across porcine skin in vitro. Liquid chromatography-mass spectrometric analysis was carried out using an Agilent 1200 Series HPLC system coupled to an Agilent G1969A TOF-MS system. Transdermal flux values of the drugs were determined from the steady-state portion of the cumulative amount versus time curves. Following twelve hours of microneedle roller application, there was a 6.74-fold increase in the percutaneous penetration of tiagabine hydrochloride (86.42 ± 25.66 µg/cm²/h) compared to passive delivery (12.83 ± 6.30 µg/cm²/h). For carbamazepine in 20% ethanol, passive transdermal flux of 7.85 ± 0.60 µg/cm²/h was observed compared to 10.85 ± 0.11 µg/cm²/h after microneedle treatment. Carbamazepine reconstituted in 30% ethanol resulted in only a 1.19-fold increase in drug permeation across porcine skin (36.73 ± 1.83 µg/cm²/h versus 30.74 ± 1.32 µg/cm²/h). Differences in flux values of untreated and microneedle-treated porcine skin using solid microneedles for the transdermal delivery of tiagabine were statistically significant. Although there were 1.38- and 1.19-fold increases in transdermal flux values of carbamazepine when applied as 20% and 30% ethanol solutions across microneedle-treated porcine skin, respectively, the increases were not statistically significant.