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

Evidence based noninvasive presurgical evaluation for patients with drug resistant epilepsies

PURPOSE OF REVIEW

To review the current practices and evidence for the diagnostic accuracy and the benefits of presurgical evaluation.

RECENT FINDINGS

Preoperative evaluation of patients with drug-resistant focal epilepsies and subsequent epilepsy surgery leads to a significant proportion of seizure-free patients. Even those who are not completely seizure free postoperatively often experience improved quality of life with better social integration. Systematic reviews and meta-analysis on the diagnostic accuracy are available for Video-electroencephalographic (EEG) monitoring, magnetic resonance imaging (MRI), electric and magnetic source imaging, and functional MRI for lateralization of language and memory. There are currently no evidence-based international guidelines for presurgical evaluation and epilepsy surgery.

SUMMARY

Presurgical evaluation is a complex multidisciplinary and multiprofessional clinical pathway. We rely on limited consensus-based recommendations regarding the required staffing or methodological expertise in epilepsy centers.

Metabolic connectivity is associated with seizure outcome in surgically treated temporal lobe epilepsies: A 18F-FDG PET seed correlation analysis

¹⁸F-FDG PET provides high sensitivity for the pre-surgical assessment of drug-resistant temporal lobe epilepsy (TLE). However, little is known about the metabolic connectivity of epileptogenic networks involved. This study therefore aimed to evaluate the association between metabolic connectivity and seizure outcome in surgically treated TLE.

METHODS

The study included 107 right-handed patients that had undergone a presurgical interictal ¹⁸F-FDG PET assessment followed by an anterior temporal lobectomy and were classified according to seizure outcome 2 years after surgery. Metabolic connectivity was evaluated by seed correlation analysis in left and right epilepsy patients with a Class Engel IA or > IA outcome and compared to age-, sex- and handedness-matched healthy controls.

RESULTS

Increased metabolic connectivity was observed in the >IA compared to the IA group within the operated temporal lobe (respective clusters of 7.5 vs 3.3 cm³ and 2.6 cm³ vs 2.2 cm³ in left and right TLE), and to a lower extent with the contralateral temporal lobe (1.2 vs 0.7 cm³ and 1.7 cm³ vs 0.7 cm³ in left and right TLE). Seed correlations provided added value for the estimated individual performance of seizure outcome over the group comparisons in left TLE (AUC of 0.74 vs 0.67).

CONCLUSION

Metabolic connectivity is associated with outcome in surgically treated TLE with a strengthened epileptogenic connectome in patients with non-free-seizure outcomes. The added value of seed correlation analysis in left TLE underlines the importance of evaluating metabolic connectivity in network related diseases.

Latest Views on the Mechanisms of Action of Surgically Implanted Cervical Vagal Nerve Stimulation in Epilepsy

BACKGROUND

Vagus nerve stimulation (VNS) is approved as an adjunctive treatment for drug-resistant epilepsy. Although there is a substantial amount of literature aiming at unraveling the mechanisms of action of VNS in epilepsy, it is still unclear how the cascade of events triggered by VNS leads to its antiepileptic effect.

OBJECTIVE

In this review, we integrated available peer-reviewed data on the effects of VNS in clinical and experimental research to identify those that are putatively responsible for its therapeutic effect. The topic of transcutaneous VNS will not be covered owing to the current lack of data supporting the differences and commonalities of its mechanisms of action in relation to invasive VNS.

SUMMARY OF THE MAIN FINDINGS

There is compelling evidence that the effect is obtained through the stimulation of large-diameter afferent myelinated fibers that project to the solitary tract nucleus, then to the parabrachial nucleus, which in turn alters the activity of the limbic system, thalamus, and cortex. VNS-induced catecholamine release from the locus coeruleus in the brainstem plays a pivotal role. Functional imaging studies tend to point toward a common vagal network that comes into play, made up of the amygdalo-hippocampal regions, left thalamus, and insular cortex.

CONCLUSIONS

Even though some crucial pieces are missing, neurochemical, molecular, cellular, and electrophysiological changes occur within the vagal afferent network at three main levels (the brainstem, the limbic system [amygdala and hippocampus], and the cortex). At this final level, VNS notably alters functional connectivity, which is known to be abnormally high within the epileptic zone and was shown to be significantly decreased by VNS in responders. The effect of crucial VNS parameters such as frequency or current amplitude on functional connectivity metrics is of utmost importance and requires further investigation.

Prediction of the Responsiveness to Vagus-Nerve Stimulation in Patients with Drug-Resistant Epilepsy via Directed-Transfer-Function Analysis of Their Perioperative Scalp EEGs

This study aims to compare directed transfer function (DTF), which is an effective connectivity analysis, derived from scalp EEGs between responder and nonresponder groups implanted with vagus-nerve stimulation (VNS). Twelve patients with drug-resistant epilepsy (six responders and six nonresponders) and ten controls were recruited. A good response to VNS was defined as a reduction of ≥50% in seizure frequency compared with the presurgical baseline. DTF was calculated in five frequency bands (delta, theta, alpha, beta, and broadband) and seven grouped electrode regions (left and right frontal, temporal, parieto-occipital, and midline) in three different states (presurgical, stimulation-on, and stimulation-off states). Responders showed presurgical nodal strength close to the control group in both inflow and outflow, whereas nonresponders exhibited increased inward and outward connectivity measures. Nonresponders also had increased inward and outward connectivity measures in the various brain regions and various frequency bands assessed compared with the control group when the stimulation was on or off. Our study demonstrated that the presurgical DTF profiles of responders were different from those of nonresponders. Moreover, a presurgical normal DTF profile may predict good responsiveness to VNS.

Metabolic Brain Network and Surgical Outcome in Temporal Lobe Epilepsy: A Graph Theoretical Study Based on 18F-fluorodeoxyglucose PET

Drug-resistant temporal lobe epilepsy (TLE) is a potential candidate for surgery; however, nearly one-third subjects had a poor surgical prognosis. We studied the underlying neuromechanism related to the surgical prognosis using graph theory based on metabolic brain network. Sixty-four unilateral TLE subjects with preoperative ¹⁸F-fluorodeoxyglucose (FDG) PET scanning were retrospectively enrolled and divided into Ia (Engel class Ia, n = 32) and non-Ia (Engel class Ib-IV, n = 32) groups according to more than 3-year follow-up after unilateral anterior temporal lobectomy (ATL). The metabolic brain network was constructed and the changed metabolic connectivity of Ia and non-Ia was detected compared with 15 matched healthy controls (HCs). Further, the network properties, including small-worldness and global efficiency, were calculated and hub nodes were also identified for the 3 groups respectively. Non-Ia group exhibited increased connectivity between contralateral fusiform gyrus and contralateral lingual gyrus; while Ia showed decreased connectivity mainly among bilateral frontal, temporal and parietal cortex. Graph theoretical analysis revealed that non-Ia group showed increased small-worldness (35%<s < 55%, P ≤ 0.05) compared to HCs; and elevated global efficiency (P = 0.05) and decreased L_p (P = 0.05) compared to Ia group. Ia group showed reduced C_p (55%<s < 63%, P < 0.05) and increased small-worldness (35%<s < 37%, P < 0.05) compared to HCs; Furthermore, disrupted hub nodes distribution pattern with the midcingulate gyrus disappeared, was also found in non-Ia group compared with the Ia group. All those results revealed that elevated network integration and metabolic connectivity, redistributed hub nodes pattern is associated with ongoing postoperative seizures in subjects with intractable TLE.

Intrinsic Thalamic Network in Temporal Lobe Epilepsy With Hippocampal Sclerosis According to Surgical Outcomes

Background: The aim of this study was to identify the differences of intrinsic amygdala, hippocampal, or thalamic networks according to surgical outcomes in temporal lobe epilepsy (TLE) patients with hippocampal sclerosis (HS).

Methods: We enrolled 69 pathologically confirmed TLE patients with HS. All patients had pre-operative three-dimensional T1-weighted MRI using a 3.0 T scanner. We obtained the structural volumes of the amygdala nuclei, hippocampal subfields, and thalamic nuclei. Then, we investigated the intrinsic networks based on volumes of these structures using structural covariance and graph theoretical analysis.

Results: Of the 69 TLE patients with HS, 21 patients (42.1%) had poor surgical outcomes, whereas 40 patients (57.9%) had good surgical outcomes. The volumes in the amygdala nuclei, hippocampal subfields, and thalamic nuclei were not different according to surgical outcome. In addition, the intrinsic amygdala and hippocampal networks were not different between the patients with poor and good surgical outcomes. However, there was a significant difference in the intrinsic thalamic network in the ipsilateral hemisphere between them. The eccentricity and small-worldness index were significantly increased, whereas the characteristic path length was decreased in the patients with poor surgical outcomes compared to those with good surgical outcomes.

Conclusion: We successfully demonstrated significant differences in the intrinsic thalamic network in the ipsilateral hemisphere between TLE patients with HS with poor and good surgical outcomes. This result suggests that the pre-operative intrinsic thalamic network can be related with surgical outcomes in TLE patients with HS.

A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles

The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.

Neuroimaging evaluation of deep brain stimulation in the treatment of representative neurodegenerative and neuropsychiatric disorders

Brain stimulation technology has become a viable modality of reversible interventions in the effective treatment of many neurological and psychiatric disorders. It is aimed to restore brain dysfunction by the targeted delivery of specific electronic signal within or outside the brain to modulate neural activity on local and circuit levels. Development of therapeutic approaches with brain stimulation goes in tandem with the use of neuroimaging methodology in every step of the way. Indeed, multimodality neuroimaging tools have played important roles in target identification, neurosurgical planning, placement of stimulators and post-operative confirmation. They have also been indispensable in pre-treatment screen to identify potential responders and in post-treatment to assess the modulation of brain circuitry in relation to clinical outcome measures. Studies in patients to date have elucidated novel neurobiological mechanisms underlying the neuropathogenesis, action of stimulations, brain responses and therapeutic efficacy. In this article, we review some applications of deep brain stimulation for the treatment of several diseases in the field of neurology and psychiatry. We highlight how the synergistic combination of brain stimulation and neuroimaging technology is posed to accelerate the development of symptomatic therapies and bring revolutionary advances in the domain of bioelectronic medicine.

A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity

The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to explore the brain network's graph-theoretic properties or to produce the desired state based on neural state dynamics, regarding the brain as a passively responding system. However, the self-adjusting nature of neural system after treatment has not been fully considered in the brain control. In the present study, we propose a computational framework for optimal control of the brain with a self-adjustment process in the effective connectivity after treatment. The neural system is modeled to adjust its outgoing effective connectivity as activity-dependent plasticity after treatment, followed by synaptic rescaling of incoming effective connectivity. To control this neural system to induce the desired function, the system's self-adjustment parameter is first estimated, based on which the treatment is optimized. Utilizing this framework, we conducted simulations of optimal control over a functional hippocampal circuitry, estimated using dynamic causal modeling of voltage-sensitive dye imaging from the wild type and mutant mice, responding to consecutive electrical stimuli. Simulation results for optimal control of the abnormal circuit toward a healthy circuit using a single node treatment, neural-type specific treatment as an analogy of medication, and combined treatments of medication and nodal treatment suggest the plausibility of the current framework in controlling the self-adjusting neural system within a restricted treatment setting. We believe the proposed computational framework of the self-adjustment system would help optimal control of the dynamic brain after treatment.

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.

Evaluation of Functional Connectivity in the Brain Using Positron Emission Tomography: A Mini-Review

Resting-state networks (RSNs) exhibit spontaneous functional connectivity in the resting state. Previous studies have evaluated RSNs mainly based on spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signals during functional magnetic resonance imaging (fMRI). However, separation between regional increases in cerebral blood flow (CBF) and oxygen consumption is theoretically difficult using BOLD-fMRI. Such separation can be achieved using quantitative ¹⁵O-gas and water positron emission tomography (PET). In addition, ¹⁸F-FDG PET can be used to investigate functional connectivity based on changes in glucose metabolism, which reflects local brain activity. Previous studies have highlighted the feasibility and clinical usefulness of ¹⁸F-FDG-PET for the analysis of RSNs, and recent studies have utilized simultaneous PET/fMRI for such analyses. While PET provides seed information regarding the focus of the abnormalities (e.g., hypometabolism and reduced target binding), fMRI is used for the analysis of functional connectivity. Thus, as PET and fMRI provide different types of information, integrating these modalities may aid in elucidating the pathological mechanisms underlying certain diseases, and in characterizing individual patients.