The re-organization of functional brain networks in pharmaco-resistant epileptic patients who respond to VNS

A predictive model combining connectomics and entropy biomarkers to discriminate long-term vagus nerve stimulation efficacy for pediatric patients with drug-resistant epilepsy

AIMS

To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug-resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi-modal feature set with high prediction accuracy.

METHODS

Sixty-five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups.

RESULTS

The proportion of VNS responders was 58.5% (38/65) at the last follow-up. In the analysis of parieto-occipital α band activity, higher synchronization level and nodal efficiency were found in responders. The central-frontal θ band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838.

CONCLUSION

Our results revealed that, compared to nonresponders, VNS responders had a more efficient α band brain network, especially in the parieto-occipital region, and less spectral complexity of θ brain activities in the central-frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Acute Effect of Vagus Nerve Stimulation in Patients with Drug-Resistant Epilepsy: A Preliminary Exploration via Stereoelectroencephalogram

As the pathophysiological mechanisms of vagus nerve stimulation (VNS) causing individual differences in the vagal ascending network remains unclear, stereoelectroencephalography (SEEG) provides a unique platform to explore the brain networks affected by VNS and helps to understand the anti-seizure mechanism of VNS more comprehensively. This study presents a preliminary exploration of the acute effect of VNS. SEEG signals were collected to assess the acute effect of VNS on neural synchronization in patients with drug-resistant epilepsy, especially in epileptogenic networks. The results show that the better the efficacy of VNS, the wider the spread of desynchronization assessed by weighted phase lag index at a high frequency band caused by VNS. Future studies should focus on the association between the change in synchronization and the efficacy of VNS, exploring the possibility of synchronization as a biomarker for patient screening and parameter programming.

Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy

OBJECTIVE

Although vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data.

METHODS

Twenty-three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole-brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine-learning classifier to predict VNS outcomes.

RESULTS

The postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross-validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level.

SIGNIFICANCE

This study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy.

Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy

Objective

This study aims to investigate the impact of vagus nerve stimulation (VNS) on the connectivity and small-world metrics of brain functional networks during seizure periods.

Methods

Ten refractory epilepsy patients underwent video encephalographic monitoring before and after VNS treatment. The 2-min electroencephalogram segment containing the ictal was selected for each participant, resulting in a total of 20 min of seizure data. The weighted phase lag index (wPLI) and small-world metrics were calculated for the whole frequency band and different frequency bands (delta, theta, alpha, beta, and gamma). Finally, the relevant metrics were statistically analyzed, and the false discovery rate was used to correct for differences after multiple comparisons.

Results

In the whole band, the wPLI was notably enhanced, and the network metrics, including degree (D), clustering coefficient (CC), and global efficiency (GE), increased, while characteristic path length (CPL) decreased (P < 0.01). In different frequency bands, the wPLI between the parieto-occipital and frontal regions was significantly strengthened in the delta and beta bands, while the wPLI within the frontal region and between the frontal and parieto-occipital regions were significantly reduced in the beta and gamma bands (P < 0.01). In the low-frequency band (<13 Hz), the small-world metrics demonstrated significantly increased CC, D, and GE, with a significantly decreased CPL, indicating a more efficient network organization. In contrast, in the gamma band, the GE decreased, and the CPL increased, suggesting a shift toward less efficient network organization.

Conclusion

VNS treatment can significantly change the wPLI and small-world metrics. These findings contribute to a deeper understanding of the impact of VNS therapy on brain networks and provide objective indicators for evaluating the efficacy of VNS.

Estimated EEG functional connectivity and aperiodic component induced by vagal nerve stimulation in patients with drug-resistant epilepsy

Background

Vagal nerve stimulation (VNS) improves seizure frequency and quality of life in patients with drug-resistant epilepsy (DRE), although the exact mechanism is not fully understood. Previous studies have evaluated the effect of VNS on functional connectivity using the phase lag index (PLI), but none has analyzed its effect on EEG aperiodic parameters (offset and exponent), which are highly conserved and related to physiological functions.

Objective

This study aimed to evaluate the effect of VNS on PLI and aperiodic parameters and infer whether these changes correlate with clinical responses in subjects with DRE.

Materials and methods

PLI, exponent, and offset were derived for each epoch (and each frequency band for PLI), on scalp-derived 64-channel EEG traces of 10 subjects with DRE, recorded before and 1 year after VNS. PLI, exponent, and offset were compared before and after VNS for each patient on a global basis, individual scalp regions, and channels and separately in responders and non-responders. A correlation analysis was performed between global changes in PLI and aperiodic parameters and clinical response.

Results

PLI (global and regional) decreased after VNS for gamma and delta bands and increased for an alpha band in responders, but it was not modified in non-responders. Aperiodic parameters after VNS showed an opposite trend in responders vs. non-responders: both were reduced in responders after VNS, but they were increased in non-responders. Changes in aperiodic parameters correlated with the clinical response.

Conclusion

This study explored the action of VNS therapy from a new perspective and identified EEG aperiodic parameters as a new and promising method to analyze the efficacy of neuromodulation.

Neuromodulation of OCD: A review of invasive and non-invasive methods

Early research into neural correlates of obsessive compulsive disorder (OCD) has focused on individual components, several network-based models have emerged from more recent data on dysfunction within brain networks, including the the lateral orbitofrontal cortex (lOFC)-ventromedial caudate, limbic, salience, and default mode networks. Moreover, the interplay between multiple brain networks has been increasingly recognized. As the understanding of the neural circuitry underlying the pathophysiology of OCD continues to evolve, so will too our ability to specifically target these networks using invasive and noninvasive methods. This review discusses the rationale for and theory behind neuromodulation in the treatment of OCD.

A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug-resistant epilepsy

Aims

Vagus nerve stimulation (VNS) is a neuromodulation therapy for children with drug‐resistant epilepsy (DRE). The efficacy of VNS is heterogeneous. A prediction model is needed to predict the efficacy before implantation.

Methods

We collected data from children with DRE who underwent VNS implantation and received regular programming for at least 1 year. Preoperative clinical information and scalp video electroencephalography (EEG) were available in 88 children. Synchronization features, including phase lag index (PLI), weighted phase lag index (wPLI), and phase‐locking value (PLV), were compared between responders and non‐responders. We further adapted a support vector machine (SVM) classifier selected from 25 clinical and 18 synchronization features to build a prediction model for efficacy in a discovery cohort (n = 70) and was tested in an independent validation cohort (n = 18).

Results

In the discovery cohort, the average interictal awake PLI in the high beta band was significantly higher in responders than non‐responders (p < 0.05). The SVM classifier generated from integrating both clinical and synchronization features had the best prediction efficacy, demonstrating an accuracy of 75.7%, precision of 80.8% and area under the receiver operating characteristic (AUC) of 0.766 on 10‐fold cross‐validation. In the validation cohort, the prediction model demonstrated an accuracy of 61.1%.

Conclusion

This study established the first prediction model integrating clinical and baseline synchronization features for preoperative VNS responder screening among children with DRE. With further optimization of the model, we hope to provide an effective and convenient method for identifying responders before VNS implantation.

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology and disorder specificity

Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.

Vagus Nerve Stimulation Elicits Sleep EEG Desynchronization and Network Changes in Responder Patients in Epilepsy

Neural desynchronization was shown as a key mechanism of vagus nerve stimulation (VNS) action in epilepsy, and EEG synchronization measures are explored as possible response biomarkers. Since brain functional organization in sleep shows different synchrony and network properties compared to wakefulness, we aimed to explore the effects of acute VNS on EEG-derived measures in the two different states of vigilance. EEG epochs were retrospectively analyzed from twenty-four VNS-treated epileptic patients (11 responders, 13 non-responders) in calm wakefulness and stage N2 sleep. Weighted Phase Lag Index (wPLI) was computed as connectivity measure of synchronization, for VNS OFF and VNS ON conditions. Global efficiency (GE) was computed as a network measure of integration. Ratios OFF/ON were obtained as desynchronization/de-integration index. Values were compared between responders and non-responders, and between EEG states. ROC curve and area-under-the-curve (AUC) analysis was performed for response classification. In responders, stronger VNS-induced theta desynchronization (p < 0.05) and decreased GE (p < 0.05) were found in sleep, but not in wakefulness. Theta sleep wPLI Ratio OFF/ON yielded an AUC of 0.825, and 79% accuracy as a response biomarker if a cut-off value is set at 1.05. Considering all patients, the VNS-induced GE decrease was significantly more important in sleep compared to awake EEG state (p < 0.01). In conclusion, stronger sleep EEG desynchronization in theta band distinguishes responders to VNS therapy from non-responders. VNS-induced reduction of network integration occurs significantly more in sleep than in wakefulness.

A Review of Parameter Settings for Invasive and Non-invasive Vagus Nerve Stimulation (VNS) Applied in Neurological and Psychiatric Disorders

Vagus nerve stimulation (VNS) is an established form of neuromodulation with a long history of promising applications. Earliest reports of VNS in the literature date to the late 1800’s in experiments conducted by Dr. James Corning. Over the past century, both invasive and non-invasive VNS have demonstrated promise in treating a variety of disorders, including epilepsy, depression, and post-stroke motor rehabilitation. As VNS continues to rapidly grow in popularity and application, the field generally lacks a consensus on optimum stimulation parameters. Stimulation parameters have a significant impact on the efficacy of neuromodulation, and here we will describe the longitudinal evolution of VNS parameters in the following categorical progression: (1) animal models, (2) epilepsy, (3) treatment resistant depression, (4) neuroplasticity and rehabilitation, and (5) transcutaneous auricular VNS (taVNS). We additionally offer a historical perspective of the various applications and summarize the range and most commonly used parameters in over 130 implanted and non-invasive VNS studies over five applications.

Functional Connectivity Derived From Electroencephalogram in Pharmacoresistant Epileptic Encephalopathy Using Cannabidiol as Adjunctive Antiepileptic Therapy

To explore brain function using functional connectivity and network topology derived from electroencephalogram (EEG) in patients with pharmacoresistant epileptic encephalopathy with cannabidiol as adjunctive antiepileptic treatment. Sixteen epileptic patients participated in the study, six of whom had epileptic encephalopathy with a stable dose of cannabidiol Epidiolex (CBD) as adjunctive therapy. Functional connectivity derived from EEG was analyzed based on the synchronization likelihood (SL). The analysis also included reconstructing graph-theoretic measures from the synchronization matrix. Comparison of functional connectivity data between each pathological group with the control group was carried out using a nonparametric permutation test applied to SL values between pairs of electrodes for each frequency band. To compare the association patterns between graph-theoretical properties of each pathological group with the control group, Z Crawford was calculated as a measure of distance. There were differences between pairs of electrodes in all frequency bands evaluated in encephalopathy epileptic patients with CBD adjunctive therapy compared with the control (p < 0.05, permutation test). In the epileptic encephalopathy group without CBD therapy, the SL values were higher than in the control group for the beta, theta, and delta EEG frequency bands, and lower for the alpha frequency band. Interestingly, patients who had CBD as adjunctive therapy demonstrated greater synchronization for all frequency bands, showing less spatial distribution for alpha frequency compared with the control. When comparing both epileptic groups, those patients who had adjunctive CBD treatment also showed increased synchronization for all frequency bands. In epileptic encephalopathy with adjunctive CBD therapy, the pattern of differences for graph-theoretical measures according to Z Crawford indicated less segregation and greater integration suggesting a trend towards the random organization of the network principally for alpha and beta EEG bands. This exploratory study revealed a tendency to an overconnectivity with a random network topology mainly for fast EEG bands in epileptic encephalopathy patients using CBD adjunctive therapy. It can therefore be assumed that the CBD treatment could be related to inhibition of the transition of the interictal to ictal state and/or to the improvement of EEG organization and brain function.

Gray Matter Atrophy: The Impacts of Resective Surgery and Vagus Nerve Stimulation in Drug-Resistant Epilepsy

BACKGROUND

There is great concern for cognitive function after resective temporal lobe surgery for drug-resistant epilepsy. However, few studies have investigated postoperative anatomical changes, and the downstream effects of surgery are poorly understood. This study investigated volumetric changes after resective surgery and vagus nerve stimulation (VNS) for epilepsy.

METHODS

Preoperative and latest postoperative (mean, 28 months) structural T1 magnetic resonance imaging scans were retrospectively obtained for 43 patients: 27 temporal lobe resections (TLRs), 6 extratemporal lobe resections, and 10 VNS, undergoing surgery for drug-resistant epilepsy between 2012 and 2017. Automated volumetric analyses of predefined cortical gray matter and subcortical structures were performed. Preoperative and postoperative volumes were compared, and the effects of age, gender, operation type, resection laterality, selectivity, time since surgery, and seizure outcome on volumetric changes were analyzed.

RESULTS

After TLRs, there were reductions in contralateral hemispheric gray matter, temporal lobe, entorhinal cortex, parahippocampal, superior temporal, middle temporal, inferior temporal (P = 0.02), lingual, fusiform, precentral, paracentral, postcentral, pericalcarine gyri, and ipsilateral superior parietal gyrus. After VNS, there was bilateral atrophy in the thalamus, putamen, cerebellum, rostral anterior cingulate, posterior cingulate, medial orbitofrontal, paracentral, fusiform, and transverse temporal gyri. There was a significant effect of surgery type but no effect of age, gender, operation type, resection laterality, selectivity, time since surgery, and seizure outcome on contralateral hippocampal gray matter change.

CONCLUSION

This is the first study to demonstrate volumetric decreases in temporal and connected regions after TLRs and VNS. These results provide interesting insight into functional network changes.

Preliminary analysis of the effect of vagus nerve stimulation in the treatment of children with intractable epilepsy

BACKGROUND

Implant vagus nerve stimulation is an adjunctive treatment for intractable epilepsy when patients are not suitable for resective surgery.

AIM

To identify the safety and efficacy of vagus nerve stimulation in children with intractable epilepsy and analyze the effects on different epilepsy syndromes.

METHODS

Eligible children with intractable epilepsy were admitted to the study. We collected data from preoperative assessments as the baseline. During the follow-up time, we recorded the process of seizures (frequency, duration, and seizure type), the changes of drugs or parameters, the complications, etc. The mean reduction rate of seizures, response rate, and McHugh scale were chosen as the outcomes.

RESULTS

A total of 213 patients were implanted with Tsinghua Pins vagus nerve stimulators, and the average age was 6.6 years. In the follow-up time of postoperative 3 mo, 6 mo, 12 mo, 18 mo, and 24 mo, the average reduction rate was 30.2%, 49.5%, 56.3%, 59.4%, and 63.2%, while the response rate was 21.8%, 62.5%, 57.1%, 69.2%, and 70.7%. In addition, implanted vagus nerve stimulation had different effects on epilepsy syndromes. The reduction rate of West syndrome increased from 36.4% (postoperative 6 m) to 74.3% (postoperative 24 m). The reduction rate of Lennox-Gastaut syndrome improved from 25.4% to 73.1% in 24 mo. The chi-square test of the five efficacy grades showed P < 0.05. The comparison between the 3-mo follow-up and the 6-mo follow-up showed P < 0.05, and the comparison between the 6-mo follow-up and the 24-mo follow-up showed P > 0.05.

CONCLUSION

Vagus nerve stimulation is safe and effective in children with intractable epilepsy, and the seizure reduction occurred in a time-dependent manner. Moreover, patients with West syndrome may get the most benefits.

The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization

Introduction: VNS is an adjunctive neuromodulation therapy for patients with drug-refractory epilepsy. The antiseizure effect of VNS is thought to be related to a diffuse modulation of functional connectivity but remains to be confirmed.

Aim: To investigate electroencephalographic (EEG) metrics of functional connectivity in patients with drug-refractory epilepsy treated by vagus nerve stimulation (VNS), between VNS-stimulated “ON” and nonstimulated “OFF” periods and between responder (R) and nonresponder (NR) patients.

Methods: Scalp-EEG was performed for 35 patients treated by VNS, using 21 channels and 2 additional electrodes on the neck to detect the VNS stimulation. Patients were defined as VNS responders if a reduction of seizure frequency of ∼50% was documented. We analyzed the synchronization in EEG time series during “ON” and “OFF” periods of stimulation, using average phase lag index (PLI) in signal space and phase-locking value (PLV) between 10 sources. Based on graph theory, we computed brain network models and analyzed minimum spanning tree (MST) for responder and nonresponder patients.

Results: Among 35 patients treated by VNS for a median time of 7 years (range 4 months to 22 years), 20 were R and 15 were NR. For responder patients, PLI during ON periods was significantly lower than that during OFF periods in delta (p = 0.009), theta (p = 0.02), and beta (p = 0.04) frequency bands. For nonresponder patients, there were no significant differences between ON and OFF periods. Moreover, variations of seizure frequency with VNS correlated with the PLI OFF/ON ratio in delta (p = 0.02), theta (p = 0.04), and beta (p = 0.03) frequency bands. Our results were confirmed using PLV in theta band (p < 0.05). No significant differences in MST were observed between R and NR patients.

Conclusion: The correlation between VNS-induced interictal EEG time-series desynchronization and decrease in seizure frequency suggested that VNS therapeutic impact might be related to changes in interictal functional connectivity.

Biomarkers of seizure response to vagus nerve stimulation: A scoping review

Although vagus nerve stimulation (VNS) is a common procedure, seizure outcomes are heterogeneous, with few available means to preoperatively identify the ideal surgical candidate. Here, we perform a scoping review of the literature to identify biomarkers of VNS response in patients with drug-resistant epilepsy. Several databases (Ovid MEDLINE, Ovid Embase, BIOSIS Previews, and Web of Science) were searched for all relevant articles that reported at least one biomarker of VNS response following implantation for intractable epilepsy. Patient demographics, seizure data, and details related to biomarkers were abstracted from all studies. From the 288 records screened, 28 articles reporting on 16 putative biomarkers were identified. These were grouped into four categories: network/connectomic-based biomarkers, electrophysiological signatures, structural findings on neuroimaging, and systemic assays. Differences in brain network organization, connectivity, and electrophysiological synchronicity demonstrated the most robust ability to identify VNS responders. Structural findings on neuroimaging yielded inconsistent associations with VNS responsiveness. With regard to systemic biomarkers, heart rate variability was shown to be an independent marker of VNS response, whereas inflammatory markers were not useful. There is an unmet need to preoperatively identify candidates who are likely to benefit from VNS. Several biomarkers demonstrate promise in predicting seizure responsiveness to VNS, particularly measures of brain network connectivity. Further efforts are required to validate existing biomarkers to inform clinical decision-making.

Alteration in brain connectivity in patients with Dravet syndrome after vagus nerve stimulation (VNS): exploration of its effectiveness using graph theory analysis with electroencephalography

OBJECTIVE

Vagus nerve stimulation (VNS) is a nonpharmacologic therapeutic option for patients who have pharmaco-resistant Dravet syndrome (DS). Plentiful efforts have been made for delivering VNS to DS patients, but its effectiveness still requires further verification. We investigated the effectiveness of the VNS treatment of DS patients using brain connectivity analysis with electroencephalography (EEG).

APPROACH

Twenty pharmaco-resistant DS patients were selected to undergo VNS implantation and classified into responder and non-responder groups after 24 months post-VNS. The effect of VNS between 6 months pre- and 6, 12, and 24 months post-VNS in all patients, responders, and non-responders on four different frequency categories of four brain parameters were compared using resting-state EEG.

MAIN RESULTS

In alpha and beta bands, all patients showed positive results for characteristic path length (CPL), global efficiency (GE), and transitivity after VNS treatment, and changes in betweenness centrality (BC) were not significant. The difference in transitivity between responders and non-responders is more pronounced than those in CPL and GE are, in both the alpha (p < 0.015) and beta (p < 0.001) bands. There was an obvious change in BC, especially in the alpha band, as the hubs tended to move from frontal lobe to parietal lobe for responders; however, there was no change for the non-responders.

SIGNIFICANCE

We investigated the alteration in brain connectivity of DS patients in alpha and beta bands during a long-term follow-up and found the responders have a decreased transitivity after the VNS treatment. Moreover, the hubs with high values in the alpha band tended to move from frontal lobe to parietal lobe for responders after VNS treatment.

Vagus nerve stimulation for pediatric patients with drug-resistant epilepsy caused by genetic mutations: Two cases

Vagus nerve stimulation (VNS) is a neuromodulation therapy increasingly used for treating drug-resistant epilepsy. However, it remains to be determined which patients are best suited for the treatment, and it is difficult to predict the therapeutic effect before the implantation. Mutations in some genes could lead to epilepsy. Here we report two cases of pediatric patients with drug-resistant epilepsy treated by VNS therapy: Patient 1 with ARX mutation achieved good outcomes; Patient 2 with the CDKL5 mutation did not show improvement. Additionally, the therapeutic impact of VNS on brain networks was investigated, hoping to provide some empirical evidence for a better understanding of the mechanism of VNS treatment.

Effect of short-term transcutaneous trigeminal nerve stimulation on EEG activity in drug-resistant epilepsy

BACKGROUND

Transcutaneous trigeminal nerve stimulation (TNS) has antiepileptic effects in patients with drug-resistant epilepsy (DRE). However, whether and how TNS is able to modulate the electroencephalogram (EEG) background activity in patients with DRE is still unknown.

OBJECTIVES

To investigate the effect of short-term TNS on EEG background activity in DRE by qualitative and quantitative analyses.

METHODS

Twenty-nine DRE patients participated in the study. Twenty-two were randomly divided into a "sham-TNS" or "real-TNS" group; seven patients underwent stimulation of the median nerve (MNS) at the wrist. Real-TNS was delivered bilaterally to the infraorbital nerve (trains of 1-20 mA, 120 Hz, cyclic modality for 20 min). The sham-TNS protocol mimicked the real-TNS one but at a zero intensity. For MNS, the same parameters as real-TNS were used. EEG was continuously acquired for 40 min: 10' pre, 20' during and 10' post stimulation. EEG was visually inspected for interictal epileptiform discharge (IEDs) changes and processed by spectral analysis for changes in mean frequency and absolute power of each frequency band.

RESULTS

A significant increase of EEG absolute alpha power was observed during real-TNS compared with the sham-TNS (F_34,680 = 1.748; p = 0.006). Conversely, no significant effects were noticed either for quantitative analysis of other frequency bands or for IEDs detection. MNS proved unable to modulate EEG activity.

CONCLUSIONS

Short-term TNS induces an acute and specific effect on background EEG of DRE by increasing the absolute alpha band power. EEG alpha rhythm enhancement may index a cortical functional inhibition and act as a seizure-preventing mechanism.

Amnestic Mild Cognitive Impairment Is Associated With Frequency-Specific Brain Network Alterations in Temporal Poles

There is general agreement that the neuropathological processes leading to Alzheimer’s disease (AD) begin decades before the clinical onset. In order to detect early topological changes, we applied functional connectivity and network analysis to magnetoencephalographic (MEG) data obtained from 16 patients with amnestic Mild Cognitive Impairment (aMCI), a prodromal stage of AD, and 16 matched healthy control (HCs). Significant differences between the two groups were found in the theta band, which is associated with memory processes, in both temporal poles (TPs). In aMCI, the degree and betweenness centrality (BC) were lower in the left superior TP, whereas in the right middle TP the BC was higher. A statistically significant negative linear correlation was found between the BC of the left superior TP and a delayed recall score, a sensitive marker of the “hippocampal memory” deficit in early AD. Our results suggest that the TPs, which are involved early in AD pathology and belong to the memory circuitry, have an altered role in the functional network in aMCI.

Juvenile myoclonic epilepsy has hyper dynamic functional connectivity in the dorsolateral frontal cortex

PURPOSE

Characterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach.

METHODS

Whole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density.

RESULTS

The static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results.

CONCLUSION

Incorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME.

Interregional metabolic connectivity of 2-deoxy-2[18 F]fluoro-D-glucose positron emission tomography in vagus nerve stimulation for pediatric patients with epilepsy: A retrospective cross-sectional study

OBJECTIVE

With the recognition of epilepsy as a network disease that disrupts the organizing ability of resting-state brain networks, vagus nerve stimulation (VNS) may control epileptic seizures through modulation of functional connectivity. We evaluated preoperative 2-deoxy-2[¹⁸ F]fluoro-D-glucose (FDG) positron emission tomography (PET) in VNS-implanted pediatric patients with refractory epilepsy to analyze the metabolic connectivity of patients and its prognostic role in seizure control.

METHODS

Preoperative PET data of 66 VNS pediatric patients who were followed up for a minimum of 1 year after the procedure were collected for the study. Retrospective review of the patients' charts was performed, and five patients with inappropriate PET data or major health issues were excluded. We conducted an independent component analysis of FDG-PET to extract spatial metabolic components and their activities, which were used to perform cross-sectional metabolic network analysis. We divided the patients into VNS-effective and VNS-ineffective groups (VNS-effective group, ≥50% seizure reduction; VNS-ineffective group, <50% reduction) and compared metabolic connectivity differences between groups using a permutation test.

RESULTS

Thirty-four (55.7%) patients showed >50% seizure reduction from baseline frequency 1 year after VNS. A significant difference in metabolic connectivity evaluated by preoperative FDG-PET was noted between groups. Relative changes in glucose metabolism were strongly connected among the areas of brainstem, cingulate gyrus, cerebellum, bilateral insula, and putamen in patients with <50% seizure control after VNS.

SIGNIFICANCE

This study shows that seizure outcome of VNS may be influenced by metabolic connectivity, which can be obtained from preoperative PET imaging. This study of metabolic connectivity analysis may contribute in further understanding of the mechanism of VNS in intractable seizures.

A comparison between scalp- and source-reconstructed EEG networks

EEG can be used to characterise functional networks using a variety of connectivity (FC) metrics. Unlike EEG source reconstruction, scalp analysis does not allow to make inferences about interacting regions, yet this latter approach has not been abandoned. Although the two approaches use different assumptions, conclusions drawn regarding the topology of the underlying networks should, ideally, not depend on the approach. The aim of the present work was to find an answer to the following questions: does scalp analysis provide a correct estimate of the network topology? how big are the distortions when using various pipelines in different experimental conditions? EEG recordings were analysed with amplitude- and phase-based metrics, founding a strong correlation for the global connectivity between scalp- and source-level. In contrast, network topology was only weakly correlated. The strongest correlations were obtained for MST leaf fraction, but only for FC metrics that limit the effects of volume conduction/signal leakage. These findings suggest that these effects alter the estimated EEG network organization, limiting the interpretation of results of scalp analysis. Finally, this study also suggests that the use of metrics that address the problem of zero lag correlations may give more reliable estimates of the underlying network topology.

Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology

Vagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate the impact of epilepsy on brain networks and identify alteration of these networks after different treatments; however, there is no study to date utilizing this modality to predict the efficacy of VNS treatment. We investigated whether the rs-MEG network topology before VNS implantation can be used to predict efficacy of VNS treatment. Twenty-three patients with epilepsy who had MEG before VNS implantation were included in this study. We also included 89 healthy control subjects from the Human Connectome Project. Using the phase-locking value in the theta, alpha, and beta frequency bands as a measure of rs-MEG functional connectivity, we calculated three global graph measures: modularity, transitivity, and characteristic path length (CPL). Our results revealed that the rs-MEG graph measures were significantly heritable and had an overall good test-retest reliability, and thus these measures may be used as potential biomarkers of the network topology. We found that the modularity and transitivity in VNS responders were significantly larger and smaller, respectively, than those observed in VNS non-responders. We also observed that the modularity and transitivity in three frequency bands and CPL in delta and beta bands were significantly different in controls than those found in responders or non-responders, although the values of the graph measures in controls were closer to those of responders than non-responders. We used the modularity and transitivity as input features of a naïve Bayes classifier, and achieved an accuracy of 87% in classification of non-responders, responders, and controls. The results of this study revealed that MEG-based graph measures are reliable biomarkers, and that these measures may be used to predict seizure outcome of VNS treatment.

The Hierarchy of Brain Networks Is Related to Insulin Growth Factor-1 in a Large, Middle-Aged, Healthy Cohort: An Exploratory Magnetoencephalography Study

Recently, a large study demonstrated that lower serum levels of insulin growth factor-1 (IGF-1) relate to brain atrophy and to a greater risk for developing Alzheimer's disease in a healthy elderly population. We set out to test if functional brain networks relate to IGF-1 levels in the middle aged. Hence, we studied the association between IGF-1 and magnetoencephalography-based functional network characteristics in a middle-aged population. The functional connections between brain areas were estimated for six frequency bands (delta, theta, alpha1, alpha2, beta, gamma) using the phase lag index. Subsequently, the topology of the frequency-specific functional networks was characterized using the minimum spanning tree. Our results showed that lower levels of serum IGF-1 relate to a globally less integrated functional network in the beta and theta band. The associations remained significant when correcting for gender and systemic effects of IGF-1 that might indirectly affect the brain. The value of this exploratory study is the demonstration that lower levels of IGF-1 are associated with brain network topology in the middle aged.

Sculpting the Sculptors: Methods for Studying the Fetal Cholinergic Signaling on Systems and Cellular Scales

The non-neuronal, immunological effects of the cholinergic signaling are exerted on the system's scale of observation via the vagus nerve and on the cellular scale via α7 nicotinic acetylcholine receptor (nAChR) signaling in myeloid cells of the periphery or brain's microglia and astrocytes. The developmental effects of such multi-scale signaling can be conceived of as an example of psychoneuroimmunological (PNI) homeokinesis and, while reported in the literature, are not yet systematically well studied. To be better understood, the intricacy of the multi-scale interactions requires relevant preclinical animal models. Chronically instrumented non-anesthetized fetal sheep model comes with a strong track record of bench-to-bed translation and a large body of evidence for its strong resemblance to and relevance for human physiology on various scales of organization. Recently, there has been growing interest in pleiotropic effects of vagus nerve stimulation (VNS) on various organ systems such as innate immunity, metabolism, and emotion with implications for programming of PNI phenotype. Here we describe the procedures required to record and manipulate the vagus nerve activity in this large pregnant mammalian organism. Extending this in vivo model to in vitro, on the cellular scale, we present the method to manipulate the cholinergic signaling in ovine fetal microglia and astrocytes and analyze their responses on protein and RNA levels. Together these models can provide multi-scale-level mechanistic insights into the effects of cholinergic signaling on PNI phenotype.

α7 nicotinic acetylcholine receptor signaling modulates the inflammatory phenotype of fetal brain microglia: first evidence of interference by iron homeostasis

Neuroinflammation in utero may result in life-long neurological disabilities. Microglia play a pivotal role, but the mechanisms are poorly understood. No early postnatal treatment strategies exist to enhance neuroprotective potential of microglia. We hypothesized that agonism on α7 nicotinic acetylcholine receptor (α7nAChR) in fetal microglia will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of α7nAChR will achieve the opposite. Using an in vivo - in vitro model of developmental programming of neuroinflammation induced by lipopolysaccharide (LPS), we validated this hypothesis in primary fetal sheep microglia cultures re-exposed to LPS in presence of a selective α7nAChR agonist or antagonist. Our RNAseq and protein level findings show that a pro-inflammatory microglial phenotype acquired in vitro by LPS stimulation is reversed with α7nAChR agonistic stimulation. Conversely, antagonistic α7nAChR stimulation potentiates the pro-inflammatory microglial phenotype. Surprisingly, under conditions of LPS double-hit an interference of a postulated α7nAChR - ferroportin signaling pathway may impede this mechanism. These results suggest a therapeutic potential of α7nAChR agonists in early re-programming of microglia in neonates exposed to in utero inflammation via an endogenous cerebral cholinergic anti-inflammatory pathway. Future studies will assess the role of interactions between inflammation-triggered microglial iron sequestering and α7nAChR signaling in neurodevelopment.

Resting-state functional connectivity changes due to acute and short-term valproic acid administration in the baboon model of GGE

Resting-state functional connectivity (FC) is altered in baboons with genetic generalized epilepsy (GGE) compared to healthy controls (CTL). We compared FC changes between GGE and CTL groups after intravenous injection of valproic acid (VPA) and following one-week of orally administered VPA. Seven epileptic (2 females) and six CTL (3 females) baboons underwent resting-state fMRI (rs-fMRI) at 1) baseline, 2) after intravenous acute VPA administration (20 mg/kg), and 3) following seven-day oral, subacute VPA therapy (20–80 mg/kg/day). FC was evaluated using a data-driven approach, while regressing out the group-wise effects of age, gender and VPA levels. Sixteen networks were identified by independent component analysis (ICA). Each network mask was thresholded (z > 4.00; p < 0.001), and used to compare group-wise FC differences between baseline, intravenous and oral VPA treatment states between GGE and CTL groups. At baseline, FC was increased in most cortical networks of the GGE group but decreased in the thalamic network. After intravenous acute VPA, FC increased in the basal ganglia network and decreased in the parietal network of epileptic baboons to presumed nodes associated with the epileptic network. After oral VPA therapy, FC was decreased in GGE baboons only the orbitofrontal networks connections to the primary somatosensory cortices, reflecting a reversal from baseline comparisons. VPA therapy affects FC in the baboon model of GGE after a single intravenous dose—possibly by facilitating subcortical modulation of the epileptic network and suppressing seizure generation—and after short-term oral VPA treatment, reversing the abnormal baseline increases in FC in the orbitofrontal network. While there is a need to correlate these FC changes with simultaneous EEG recording and seizure outcomes, this study demonstrates the feasibility of evaluating rs-fMRI effects of antiepileptic medications even after short-term exposure.

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This resting-state fMRI study evaluates treatment-related functional connectivity (FC) changes in the baboon model of GGE.

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Pre-treatment FC is mostly increased in cortical networks, but decreased for the thalamic network in epileptic baboons.

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Treatment-related FC changes were noted both after single intravenous dose of VPA and short-term oral VPA treatment.

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FC studies may provide a novel approach to evaluate antiepileptic medication effects.

MEG Beamformer-Based Reconstructions of Functional Networks in Mild Cognitive Impairment

Subjects with mild cognitive impairment (MCI) have an increased risk of developing Alzheimer’s disease (AD), and their functional brain networks are presumably already altered. To test this hypothesis, we compared magnetoencephalography (MEG) eyes-closed resting-state recordings from 29 MCI subjects and 29 healthy elderly subjects in the present exploratory study. Functional connectivity in different frequency bands was assessed with the phase lag index (PLI) in source space. Normalized weighted clustering coefficient (normalized Cw) and path length (normalized Lw), as well as network measures derived from the minimum spanning tree [MST; i.e., betweenness centrality (BC) and node degree], were calculated. First, we found altered PLI values in the lower and upper alpha bands in MCI patients compared to controls. Thereafter, we explored network differences in these frequency bands. Normalized Cw and Lw did not differ between the groups, whereas BC and node degree of the MST differed, although these differences did not survive correction for multiple testing using the False Discovery Rate (FDR). As an exploratory study, we may conclude that: (1) the increases and decreases observed in PLI values in lower and upper alpha bands in MCI patients may be interpreted as a dual pattern of disconnection and aberrant functioning; (2) network measures are in line with connectivity findings, indicating a lower efficiency of the brain networks in MCI patients; (3) the MST centrality measures are more sensitive to detect subtle differences in the functional brain networks in MCI than traditional graph theoretical metrics.

EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. In contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganization is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients.

EEG Derived Brain Activity Reflects Treatment Response from Vagus Nerve Stimulation in Patients with Epilepsy

The mechanism of action of vagus nerve stimulation (VNS) is yet to be elucidated. To that end, the effects of VNS on the brain of epileptic patients were studied. Both when VNS was switched "On" and "Off", the brain activity of responders (R, seizure frequency reduction of over 50%) was compared to the brain activity of nonresponders (NR, seizure frequency reduction of less than 50%). Using EEG recordings, a significant increase in P300 amplitude for R and a significant decrease in P300 amplitude for NR were found. We found biomarkers for checking the efficacy of VNS with accuracy up to 94%. The results show that P300 features recorded in nonmidline electrodes are better P300 biomarkers for VNS efficacy than P300 features recorded in midline electrodes. Using source localization and connectivity analyses, the activity of the limbic system, insula and orbitofrontal cortex was found to be dependent on VNS switched "On" versus "Off" or patient group (R versus NR). The results suggest an important role for these areas in the mechanism of action of VNS, although a larger patient study should be done to confirm the findings.

The role of the gut-brain axis in alcohol use disorders

Neuroimmune and inflammatory processes have been locally associated with the amygdala in alcohol exposure and withdrawal. We and others have suggested that this inflammation in the amygdala may cause disturbance of neural function observed as anxiety and autonomic distress in withdrawal. Despite the potential importance of the robust neuroinflammatory response, the mechanisms contributing to this response are not well understood. We review literature that suggests the effects of alcohol, and other substances of abuse, cause dysbiosis of the gut microbiome. This peripheral response may modulate neuroprotective vagal afferent signaling that permits and exacerbates a neuroinflammatory response in the amygdala. We will examine the mounting evidence that suggests that (1) gut dysbiosis contributes to neuroinflammation, especially in the context of alcohol exposure and withdrawal, (2) the neuroinflammation in the amygdala involves the microglia and astrocytes and their effect on neural cells, and (3) amygdala neuroinflammation itself contributes directly to withdrawal behavior and symptoms. The contribution of the gut to an anxiogenic response is a promising therapeutic target for patients suffering with withdrawal symptoms given the safe and well-established methods of modulating the gut microbiome.

Neurological Diseases from a Systems Medicine Point of View

The difficulty to understand, diagnose, and treat neurological disorders stems from the great complexity of the central nervous system on different levels of physiological granularity. The individual components, their interactions, and dynamics involved in brain development and function can be represented as molecular, cellular, or functional networks, where diseases are perturbations of networks. These networks can become a useful research tool in investigating neurological disorders if they are properly tailored to reflect corresponding mechanisms. Here, we review approaches to construct networks specific for neurological disorders describing disease-related pathology on different scales: the molecular, cellular, and brain level. We also briefly discuss cross-scale network analysis as a necessary integrator of these scales.