Presurgical thalamic "hubness" predicts surgical outcome in temporal lobe epilepsy

Association Between Postsurgical Functional Connectivity and Seizure Outcome in Patients With Temporal Lobe Epilepsy

BACKGROUND AND OBJECTIVES

Despite the success of presurgical network connectivity studies in predicting short-term (1-year) seizure outcomes, later seizure recurrence occurs in some patients with temporal lobe epilepsy (TLE). To uncover contributors to this recurrence, we investigated the relationship between functional connectivity and seizure outcomes at different time points after surgery in these patients.

METHODS

Patients included were clinically diagnosed with unilateral mesial TLE after a standard clinical evaluation and underwent selective amygdalohippocampectomy. Healthy controls had no history of seizures or head injury. Using resting-state fMRI, we assessed the postsurgical functional connectivity node strength, computed as the node's total strength to all other nodes, between seizure-free (Engel Ia-Ib) and nonseizure-free (Engel Ic-IV) acquisitions. The change over time after surgery in different outcome groups in these nodes was also characterized.

RESULTS

Patients with TLE (n = 32, mean age: 43.1 ± 11.9 years; 46.8% female) and 85 healthy controls (mean age: 37.7 ± 13.5 years; 48.2% female) were included. Resting fMRI was acquired before surgery and at least once after surgery in each patient (range 1-4 scans, 5-60 months). Differences between patients with (n = 30) and without (n = 18) seizure freedom were detected in the posterior insula ipsilateral to the resection (I-PIns: 95% CI -154.8 to -50.1, p = 2.8 × 10-4) and the bilateral central operculum (I-CO: 95% CI -163.2 to -65.1, p = 2.6 × 10-5, C-CO: 95% CI -172.7 to -55.8, p = 2.8 × 10-4). In these nodes, only those who were seizure-free had increased node strength after surgery that increased linearly over time (I-CO: 95% CI 1.0-5.2, p = 4.2 × 10-3, C-CO: 95% CI 1.0-5.2, p = 5.5 × 10-3, I-PIns: 95% CI 1.6-5.5, p = 0.9 × 10-3). Different outcome groups were not distinguished by node strength before surgery.

DISCUSSION

The findings suggest that network evolution in the first 5 years after selective amygdalohippocampectomy surgery is related to seizure outcomes in TLE. This highlights the need to identify presurgical and surgical conditions that lead to disparate postsurgical trajectories between seizure-free and nonseizure-free patients to identify potential contributors to long-term seizure outcomes. However, the lack of including other surgical approaches may affect the generalizability of the results.

Centromedian region thalamic responsive neurostimulation mitigates idiopathic generalized and multifocal epilepsy with focal to bilateral tonic-clonic seizures

OBJECTIVE

Although >30% of epilepsy patients have drug-resistant epilepsy (DRE), typically those with generalized or multifocal disease have not traditionally been considered surgical candidates. Responsive neurostimulation (RNS) of the centromedian (CM) region of the thalamus now appears to be a promising therapeutic option for this patient population. We present outcomes following CM RNS for 13 patients with idiopathic generalized epilepsy (IGE) and eight with multifocal onsets that rapidly generalize to bilateral tonic-clonic (focal to bilateral tonic-clonic [FBTC]) seizures.

METHODS

A retrospective review of all patients undergoing bilateral CM RNS by the senior author through July 2022 were reviewed. Electrodes were localized and volumes of tissue activation were modeled in Lead-DBS. Changes in patient seizure frequency were extracted from electronic medical records.

RESULTS

Twenty-one patients with DRE underwent bilateral CM RNS implantation. For 17 patients with at least 1 year of postimplantation follow-up, average seizure reduction from preoperative baseline was 82.6% (SD = 19.0%, median = 91.7%), with 18% of patients Engel class 1, 29% Engel class 2, 53% Engel class 3, and 0% Engel class 4. There was a trend for average seizure reduction to be greater for patients with nonlesional FBTC seizures than for other patients. For patients achieving at least Engel class 3 outcome, median time to worthwhile seizure reduction was 203.5 days (interquartile range = 110.5-343.75 days). Patients with IGE with myoclonic seizures had a significantly shorter time to worthwhile seizure reduction than other patients. The surgical targeting strategy evolved after the first four subjects to achieve greater anatomic accuracy.

SIGNIFICANCE

Patients with both primary and rapidly generalized epilepsy who underwent CM RNS experienced substantial seizure relief. Subsets of these patient populations may particularly benefit from CM RNS. The refinement of lead targeting, tuning of RNS system parameters, and patient selection are ongoing areas of investigation.

Safety of Concomitant Cortical and Thalamic Stereoencephalography Explorations in Patients With Drug-Resistant Epilepsies

BACKGROUND AND OBJECTIVES

Intracranial electrophysiology of thalamic nuclei has demonstrated involvement of thalamic areas in the propagation of seizures in focal drug-resistant epilepsy. Recent studies have argued that thalamus stereoencephalography (sEEG) may aid in understanding the epileptogenic zone and treatment options. However, the study of thalamic sEEG-associated hemorrhage incidence has not been investigated in a cohort study design. In this article, we present the largest retrospective cohort study of sEEG patients and compare hemorrhage rates between those with and without thalamic sEEG monitoring.

METHODS

Retrospective chart review of clinical and epilepsy history, electrode implantation, rationale, and outcomes was performed for 76 patients (age 20-69 years) with drug-resistant epilepsy who underwent sEEG monitoring at our institution (2019-2022). A subset of 38% of patients (n = 30) underwent thalamic monitoring of the anterior thalamic nucleus (n = 14), pulvinar nucleus (n = 25), or both (n = 10). Planned perisylvian orthogonal sEEG trajectories were extended to 2- to 3-cm intraparenchymally access thalamic area(s).The decision to incorporate thalamic monitoring was made by the multidisciplinary epilepsy team. Statistical comparison of hemorrhage rate, type, and severity between patients with and without thalamic sEEG monitoring was made.

RESULTS

Our approach for thalamic monitoring was not associated with local intraparenchymal hemorrhage of thalamic areas or found along extended cortical trajectories, and symptomatic hemorrhage rates were greater for patients with thalamic coverage (10% vs 0%, P = .056), although this was not found to be significant. Importantly, patients with perisylvian electrode trajectories, with or without thalamic coverage, did not experience a higher incidence of hemorrhage ( P = .34).

CONCLUSION

sEEG of the thalamus is a safe and valuable tool that can be used to interrogate the efficacy of thalamic neuromodulation for drug-resistant epilepsy. While patients with thalamic sEEG did have higher incidence of hemorrhage at any monitoring site, this finding was apparently not related to the method of perisylvian implantation and did not involve any trajectories targeting the thalamus.

Mapping Functional Connectivity Signatures of Pharmacoresistant Focal Cortical Dysplasia-Related Epilepsy

OBJECTIVE

To determine common network alterations in focal cortical dysplasia pharmacoresistant epilepsy (FCD-PRE) using functional connectivity analysis of resting-state functional magnetic resonance imaging (rsfMRI).

METHODS

This is a retrospective imaging cohort from Children's National Hospital (Washington, DC, USA) from January, 2011 to January, 2022. Patients with 3-T MRI-confirmed FCD-PRE underwent rsfMRI as part of routine clinical care. Patients were included if they were age 5-22 years at the time of the scan, and had a minimum of 18 months of follow-up. Healthy, typically-developing controls were included from Children's National Hospital (n = 16) and matched from Human Connectome Project-Development public dataset (n = 100).

RESULTS

A total of 42 FCD-PRE patients (20 M:22 F, aged 14.2 ± 4.1 years) and 116 healthy controls (56 M:60 F, aged 13.7 ± 3.3 years) with rsfMRI were included. Seed-based functional connectivity maps were generated for each FCD, and each seed was used to generate a patient-specific z-scored connectivity map on 116 controls. FCD-PRE patients had mutual altered connectivity in regions of dorsal attention, default mode, and control networks. Functional connectivity was diminished within the FCD dominant functional network, as well as in homotopic regions. Cluster specific connectivity patterns varied by pathological subtype. Higher FCD connectivity to the limbic network was associated with increased odds of Engel I outcome.

INTERPRETATION

This study demonstrates diminished functional connectivity patterns in FCD-PRE, which may represent a neuromarker for the disease, independent of FCD location, involving the dorsal attention, default mode, and control functional networks. Higher connectivity to the limbic network is associated with a seizure-free outcome. Future multicenter, prospective studies are needed to allow for much earlier detection of signatures of treatment-resistant epilepsy. ANN NEUROL 2024.

Region-specific MRI predictors of surgical outcome in temporal lobe epilepsy

OBJECTIVE

In drug-resistant temporal lobe epilepsy (TLE), it is not well-established in how far surgery should target morphological anomalies to achieve seizure freedom. Here, we assessed interactions between structural brain compromise and surgery to identify region-specific predictors of seizure outcome.

METHODS

We obtained pre- and post-operative 3D T1-weighted MRI in 55 TLE patients who underwent selective amygdalo-hippocampectomy (SAH) or anterior temporal lobectomy (ATL) and 40 age and sex-matched healthy subjects. We measured surface-based morphological alterations of the mesiotemporal lobe structures (hippocampus, amygdala, entorhinal and piriform cortices), the neocortex and the thalamus on both pre- and post-operative MRI. Using precise co-registration, in each patient we mapped the surgical cavity onto the MRI acquired before surgery, thereby quantifying the amount of pathological tissue resected; these features, together with the preoperative morphometric data, served as input to a supervised classification algorithm for postsurgical outcome prediction.

RESULTS

On pre-operative MRI, patients who became seizure-free (TLE-SF) presented with severe ipsilateral amygdalar and hippocampal atrophy, while not seizure-free patients (TLE-NSF) displayed amygdalar hypertrophy. Stratifying patients based on the surgical approach, post-operative MRI showed similar patterns of mesiotemporal and thalamic changes, but divergent neocortical thinning affecting the parieto-temporo-occipital regions following ATL and the frontal lobes after SAH. Irrespective of the surgical approach, hippocampal atrophy on pre-operative MRI and its extent of resection were the most predictive features of seizure-freedom in 89% of patients (selected 100% across validations).

SIGNIFICANCE

Our study indicates a critical role of the extent of resection of MRI-derived hippocampal morphological anomalies on seizure outcome. Precise pre-operative quantification of the mesiotemporal lobe provides non-invasive prognostics for individualized surgery.

Stereoelectroencephalography of the Deep Brain: Basal Ganglia and Thalami

SUMMARY

Stereoelectroencephalography (SEEG) has emerged as a transformative tool in epilepsy surgery, shedding light on the complex network dynamics involved in focal epilepsy. This review explores the role of SEEG in elucidating the role of deep brain structures, namely the basal ganglia and thalamus, in epilepsy. SEEG advances understanding of their contribution to seizure generation, propagation, and control by permitting precise and minimally invasive sampling of these brain regions. The basal ganglia, comprising the subthalamic nucleus, globus pallidus, substantia nigra, and striatum, have gained recognition for their involvement in both focal and generalized epilepsy. Electrophysiological recordings reveal hyperexcitability and increased synchrony within these structures, reinforcing their role as critical nodes within the epileptic network. Furthermore, low-frequency and high-frequency stimulation of the basal ganglia have demonstrated potential in modulating epileptogenic networks. Concurrently, the thalamus, a key relay center, has garnered prominence in epilepsy research. Disrupted thalamocortical connectivity in focal epilepsy underscores its significance in seizure maintenance. The thalamic subnuclei, including the anterior nucleus, centromedian, and medial pulvinar, present promising neuromodulatory targets, suggesting pathways for personalized epilepsy therapies. The prospect of multithalamic SEEG and thalamic SEEG stimulation trials has the potential to revolutionize epilepsy management, offering tailored solutions for challenging cases. SEEG's ability to unveil the dynamics of deep brain structures in epilepsy promises enhanced and personalized epilepsy care in our new era of precision medicine. Until deep brain SEEG is accepted as a standard of care, a rigorous informed consent process remains paramount for patients for whom such an exploration is proposed.

Beyond Stereo-EEG: Is It Worth Combining Stereo-EEG With Other Diagnostic Methods?

SUMMARY

Stereo-EEG is a widely used method to improve the diagnostic precision of presurgical workup in patients with refractory epilepsy. Its ability to detect epileptic activity and identify epileptic networks largely depends on the chosen implantation strategy. Even in an ideal situation, electrodes record activity generated in <10% of the brain and contacts only record from brain tissue in their immediate proximity. In this article, the authors discuss how recording stereo-EEG simultaneously with other diagnostic methods can improve its diagnostic value in clinical and research settings. It can help overcome the limited spatial coverage of intracranial recording and better understand the sources of epileptic activity. Simultaneous scalp EEG is the most widely available method, often used to understand large epileptic networks, seizure propagation, and EEG activity occurring on the contralateral hemisphere. Simultaneous magnetoencephalography allows for more precise source localization and identification of deep sources outside the stereo-EEG coverage. Finally, simultaneous functional MRI can highlight metabolic changes following epileptic activity and help understand the widespread network changes associated with interictal activity. This overview highlights advantages and methodological challenges for all these methods. Clinical use and research applications are presented for each approach.

Artificial intelligence in epilepsy - applications and pathways to the clinic

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

Edge-wise analysis reveals white matter connectivity associated with focal to bilateral tonic-clonic seizures

OBJECTIVE

Focal to bilateral tonic-clonic seizures (FBTCS) represent a challenging subtype of focal temporal lobe epilepsy (TLE) in terms of both severity and treatment response. Most studies have focused on regional brain analysis that is agnostic to the distribution of white matter (WM) pathways associated with a node. We implemented a more selective, edge-wise approach that allowed for identification of the individual connections unique to FBTCS.

METHODS

T1-weighted and diffusion-weighted images were obtained from 22 patients with solely focal seizures (FS), 43 FBTCS patients, and 65 age/sex-matched healthy participants (HPs), yielding streamline (STR) connectome matrices. We used diffusion tensor-derived STRs in an edge-wise approach to determine specific structural connectivity changes associated with seizure generalization in FBTCS compared to matched FS and HPs. Graph theory metrics were computed on both node- and edge-based connectivity matrices.

RESULTS

Edge-wise analyses demonstrated that all significantly abnormal cross-hemispheric connections belonged to the FBTCS group. Abnormal connections associated with FBTCS were mostly housed in the contralateral hemisphere, with graph metric values generally decreased compared to HPs. In FBTCS, the contralateral amygdala showed selective decreases in the structural connection pathways to the contralateral frontal lobe. Abnormal connections in TLE involved the amygdala, with the ipsilateral side showing increases and the contralateral decreases. All the FS findings indicated higher graph metrics for connections involving the ipsilateral amygdala. Data also showed that some FBTCS connectivity effects are moderated by aging, recent seizure frequency, and longer illness duration.

SIGNIFICANCE

Data showed that not all STR pathways are equally affected by the seizure propagation of FBTCS. We demonstrated two key biases, one indicating a large role for the amygdala in the propagation of seizures, the other pointing to the prominent role of cross-hemispheric and contralateral hemisphere connections in FBTCS. We demonstrated topographic reorganization in FBTCS, pointing to the specific WM tracts involved.

The use of automated and AI-driven algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia

In drug-resistant epilepsy, magnetic resonance imaging (MRI) plays a central role in detecting lesions as it offers unmatched spatial resolution and whole-brain coverage. In addition, the last decade has witnessed continued developments in MRI-based computer-aided machine-learning techniques for improved diagnosis and prognosis. In this review, we focus on automated algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia, particularly in cases deemed as MRI negative, with an emphasis on studies with histologically validated data. In addition, we discuss imaging-derived prognostic markers, including response to anti-seizure medication, post-surgical seizure outcome, and cognitive reserves. We also highlight the advantages and limitations of these approaches and discuss future directions toward person-centered care.

Preoperative structural-functional coupling at the default mode network predicts surgical outcomes of temporal lobe epilepsy

OBJECTIVE

Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features.

METHODS

This study analyzed presurgical diffusion and functional magnetic resonance imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patients were categorized into seizure-free (SF) and non-seizure-free (nSF) groups based on postsurgical recurrence. Individual functional connectivity (FC), structural connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning model to classify surgical outcomes.

RESULTS

Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p < .05). Multiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p < .05). Models trained using the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteristic curve of .893 with an overall accuracy of .887.

SIGNIFICANCE

Presurgical hyper-SFC in the DMN was strongly associated with postoperative seizure recurrence. Furthermore, our results introduce a novel SFC-based machine learning model to precisely classify the surgical outcomes of TLE.

Imaging and Stereotactic Electroencephalography Functional Networks to Guide Epilepsy Surgery

Epilepsy surgery is a potentially curative treatment of drug-resistant epilepsy that has remained underutilized both due to inadequate referrals and incomplete localization hypotheses. The complexity of patients evaluated for epilepsy surgery has increased, thus new approaches are necessary to treat these patients. The paradigm of epilepsy surgery has evolved to match this challenge, now considering the entire seizure network with the goal of disrupting it through resection, ablation, neuromodulation, or a combination. The network paradigm has the potential to aid in identification of the seizure network as well as treatment selection.

Graph Theory and Modeling of Network Topology in Clinical Neurosurgery

The last several decades have seen a shift in understanding many neurological disorders as abnormalities in brain networks rather than specific brain regions. This conceptual revolution, coupled with advancements in computing capabilities and resources, has enabled a wealth of research on delineating and treating aberrant brain networks. One approach to network neuroscience, graph theory, involves modeling network topologies as mathematical graphs and computing various metrics that describe its characteristics. Using graph theory, researchers have derived new insights into the pathophysiology of many neuropsychiatric disorders and even developed treatments targeted at restoring network disturbances. In this chapter, we provide an overview of the principles of graph theory and how to implement it, specific applications of graph theory within clinical neurosurgery, and a discussion on the advantages and limitations of these approaches.

Evolution of Stereo-Electroencephalography at Massachusetts General Hospital

The practice of invasive monitoring for presurgical epilepsy workup has evolved at Massachusetts General Hospital (MGH) in parallel to the evolution in the field's understanding of epilepsy as a network disorder. Implantations have shifted from an emphasis on singularly finding single foci for the purpose of resection to a network-hypothesis-driven approach aiming to delineate patients' seizure networks with the goal of developing surgical interventions that disrupt critical nodes of these networks. Here, the authors review all invasive monitoring cases at MGH from April 2016 through June 2023 to describe how this paradigm shift has taken form.

Profiles of resting state functional connectivity in temporal lobe epilepsy associated with post-laser interstitial thermal therapy seizure outcomes and semiologies

Introduction

It is now understood that in focal epilepsy, impacted neural regions are not limited to the epileptogenic zone. As such, further investigation into the underlying functional connectivity (FC) patterns in those enduring Temporal Lobe Epilepsy (TLE) with Mesial Temporal Sclerosis (MTS) is imperative to understanding the intricacies of the disease.

Methods

The rsfMRIs of 17 healthy participants, 10 left-sided TLE-MTS patients with a pre-operative history of focal impaired awareness seizures (FIA), and 13 left-sided TLE-MTS patients with a pre-operative history of focal aware seizures (FA) were compared to determine the existence of distinct FC patterns with respect to seizure types. Similarly, the rsfMRIs of the above-mentioned healthy participants, 16 left-sided TLE-MTS individuals who were seizure-free (SF) 12 months postoperatively, and 16 left-sided TLE-MTS persons without seizure freedom (nSF) were interrogated. The ROI-to-ROI connectivity analysis included a total of 175 regions of interest (ROIs) and accounted for both age and duration of epileptic activity. Significant correlations were determined via two-sample t-tests and Bonferroni correction (α = 0.05).

Results

Comparisons of FA and FIA groups depicted significant correlations between the contralateral anterior cingulate gyrus, subgenual region, and the contralateral cerebellum, lobule III (p-value = 2.26e-4, mean z-score = -0.05 ± 0.28, T = -4.23). Comparisons of SF with nSF depicted two significantly paired-ROIs; the contralateral amygdala and the contralateral precuneus (p-value = 2.9e-5, mean z-score = -0.12 ± 0.19, T = 4.98), as well as the contralateral locus coeruleus and the ipsilateral intralaminar nucleus (p-value= 1.37e-4, mean z-score = 0.06 ± 0.17, T = -4.41).

Significance

FC analysis proves to be a lucrative modality for exploring unique signatures with respect to seizure types and postoperative outcomes. By furthering our understanding of the differences between epileptic phenotypes, we can achieve improvement in future treatment modalities not limited to targeting advancements.

Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder

Background

Internet gaming disorder (IGD) is a mental health issue that affects individuals worldwide. However, the lack of knowledge about the biomarkers associated with the development of IGD has restricted the diagnosis and treatment of this disorder.

Aims

We aimed to reveal the biomarkers associated with the development of IGD through resting-state brain network analysis and provide clues for the diagnosis and treatment of IGD.

Methods

Twenty-six patients with IGD, 23 excessive internet game users (EIUs) who recurrently played internet games but were not diagnosed with IGD and 29 healthy controls (HCs) performed delay discounting task (DDT) and Iowa gambling task (IGT). Resting-state functional magnetic resonance imaging (fMRI) data were also collected.

Results

Patients with IGD exhibited significantly lower hubness in the right medial orbital part of the superior frontal gyrus (ORBsupmed) than both the EIU and the HC groups. Additionally, the hubness of the right ORBsupmed was found to be positively correlated with the highest excessive internet gaming degree during the past year in the EIU group but not the IGD group; this might be the protective mechanism that prevents EIUs from becoming addicted to internet games. Moreover, the hubness of the right ORBsupmed was found to be related to the treatment outcome of patients with IGD, with higher hubness of this region indicating better recovery when undergoing forced abstinence. Further modelling analysis of the DDT and IGT showed that patients with IGD displayed higher impulsivity during the decision-making process, and impulsivity-related parameters were negatively correlated with the hubness of right ORBsupmed.

Conclusions

Our findings revealed that the impulsivity-related right ORBsupmed hubness could serve as a potential biomarker of IGD and provide clues for the diagnosis and treatment of IGD.

Predicting seizure outcome after epilepsy surgery: Do we need more complex models, larger samples, or better data?

OBJECTIVE

The accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if (1) training more complex models, (2) recruiting larger sample sizes, or (3) using data-driven selection of clinical predictors would improve our ability to predict postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative seizure outcome.

METHODS

We performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models-a logistic regression, a multilayer perceptron, and an XGBoost model-to predict 1-year postoperative seizure outcome on our data set. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N = 2000. Finally, we examined the impact of predictor selection on model performance.

RESULTS

Our logistic regression achieved an accuracy of 72% (95% confidence interval [CI] = 68%-75%, area under the curve [AUC] = .72), whereas our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP  = 67%-74%, AUCMLP  = .70; 95% CIXGBoost own  = 68%-75%, AUCXGBoost own  = .70). There was no significant difference in performance between our three models (all p > .4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI = 59%-67%, AUC = .62; pLR  = .005, pMLP  = .01, pXGBoost own  = .01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection.

SIGNIFICANCE

We show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict postoperative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.

Excessively increased thalamocortical connectivity and poor initial antiseizure medication response in epilepsy patients

Objectives

The network mechanism underlying the initial response to antiseizure medication in epilepsy has not been revealed yet. Given the central role of the thalamus in the brain network, we conducted a case-control study to investigate the association between thalamic connectivity and medication response.

Methods

We recruited 39 patients with newly diagnosed and medication-naïve epilepsy of genetic or unknown etiology, including 26 with a good response (GR group) and 13 with a poor response (PR group), and 26 matched healthy participants (control group). We measured the gray matter density (GMD) and the amplitude of low-frequency fluctuation (ALFF) of bilateral thalami. We then set each thalamus as the seed region of interest (ROI) to calculate voxel-wise functional connectivity (FC) and assessed ROI-wise effective connectivity (EC) between the thalamus and targeted regions.

Results

We found no significant difference between groups in the GMD or ALFF of bilateral thalami. However, we observed that the FC values of several circuits connecting the left thalamus and the cortical areas, including the bilateral Rolandic operculum, the left insula, the left postcentral gyrus, the left supramarginal gyrus, and the left superior temporal gyrus, differed among groups (False Discovery Rate correction, P < 0.05), with a higher value in the PR group than in the GR group and/or the control group (Bonferroni correction, P < 0.05). Similarly, both the outflow and the inflow EC in each thalamocortical circuit were higher in the PR group than in the GR group and the control group, although these differences did not remain statistically significant after applying the Bonferroni correction (P < 0.05). The FC showed a positive correlation with the corresponding outflow and inflow ECs for each circuit.

Conclusion

Our finding suggested that patients with stronger thalamocortical connectivity, potentially driven by both thalamic outflowing and inflowing information, may be more likely to respond poorly to initial antiseizure medication.

Altered metabolic-functional coupling in the epileptogenic network could predict surgical outcomes of mesial temporal lobe epilepsy

Objective

To investigate the relationship between glucose metabolism and functional activity in the epileptogenic network of patients with mesial temporal lobe epilepsy (MTLE) and to determine whether this relationship is associated with surgical outcomes.

Methods

¹⁸F-FDG PET and resting-state functional MRI (rs-fMRI) scans were performed on a hybrid PET/MR scanner in 38 MTLE patients with hippocampal sclerosis (MR-HS), 35 MR-negative patients and 34 healthy controls (HC). Glucose metabolism was measured using ¹⁸F-FDG PET standardized uptake value ratio (SUVR) relative to cerebellum; Functional activity was obtained by fractional amplitude of low-frequency fluctuation (fALFF). The betweenness centrality (BC) of metabolic covariance network and functional network were calculated using graph theoretical analysis. Differences in SUVR, fALFF, BC and the spatial voxel-wise SUVR-fALFF couplings of the epileptogenic network, consisting of default mode network (DMN) and thalamus, were evaluated by Mann-Whitney U test (using the false discovery rate [FDR] for multiple comparison correction). The top ten SUVR-fALFF couplings were selected by Fisher score to predict surgical outcomes using logistic regression model.

Results

The results showed decreased SUVR-fALFF coupling in the bilateral middle frontal gyrus (P_FDR = 0.0230, P_FDR = 0.0296) in MR-HS patients compared to healthy controls. Coupling in the ipsilateral hippocampus was marginally increased (P_FDR = 0.0802) in MR-HS patients along with decreased BC of metabolic covariance network and functional network (P_FDR = 0.0152; P_FDR = 0.0429). With Fisher score ranking, the top ten SUVR-fALFF couplings in regions from DMN and thalamic subnuclei could predict surgical outcomes with the best performance being a combination of ten SUVR-fALFF couplings with an AUC of 0.914.

Conclusion

These findings suggest that the altered neuroenergetic coupling in the epileptogenic network is associated with surgical outcomes of MTLE patients, which may provide insight into their pathogenesis and help with preoperative evaluation.

A phase 1 open-label trial evaluating focused ultrasound unilateral anterior thalamotomy for focal onset epilepsy

OBJECTIVE

Focused ultrasound ablation (FUSA) is an emerging treatment for neurological and psychiatric diseases. We describe the initial experience from a pilot, open-label, single-center clinical trial of unilateral anterior nucleus of the thalamus (ANT) FUSA in patients with treatment-refractory epilepsy.

METHODS

Two adult subjects with treatment-refractory, focal onset epilepsy were recruited. The subjects received ANT FUSA using the Exablate Neuro (Insightec) system. We determined the safety and feasibility (primary outcomes), and changes in seizure frequency (secondary outcome) at 3, 6, and 12 months. Safety was assessed by the absence of side effects, that is, new onset neurological deficits or performance deterioration on neuropsychological testing. Feasibility was defined as the ability to create a lesion within the anterior nucleus. The monthly seizure frequency was compared between baseline and postthalamotomy.

RESULTS

The patients tolerated the procedure well, without neurological deficits or serious adverse events. One patient experienced a decline in verbal fluency, attention/working memory, and immediate verbal memory. Seizure frequency reduced significantly in both patients; one patient was seizure-free at 12 months, and in the second patient, the frequency reduced from 90-100 seizures per month to 3-6 seizures per month.

SIGNIFICANCE

This is the first known clinical trial to assess the safety, feasibility, and preliminary efficacy of ANT FUSA in adult patients with treatment-refractory focal onset epilepsy.

Deep brain stimulation of thalamus for epilepsy

Neuromodulation (neurostimulation) is a relatively new and rapidly growing treatment for refractory epilepsy. Three varieties are approved in the US: vagus nerve stimulation (VNS), deep brain stimulation (DBS) and responsive neurostimulation (RNS). This article reviews thalamic DBS for epilepsy. Among many thalamic sub-nuclei, DBS for epilepsy has been targeted to the anterior nucleus (ANT), centromedian nucleus (CM), dorsomedial nucleus (DM) and pulvinar (PULV). Only ANT is FDA-approved, based upon a controlled clinical trial. Bilateral stimulation of ANT reduced seizures by 40.5% at three months in the controlled phase (p = .038) and 75% by 5 years in the uncontrolled phase. Side effects related to paresthesias, acute hemorrhage, infection, occasional increased seizures, and usually transient effects on mood and memory. Efficacy was best documented for focal onset seizures in temporal or frontal lobe. CM stimulation may be useful for generalized or multifocal seizures and PULV for posterior limbic seizures. Mechanisms of DBS for epilepsy are largely unknown, but animal work points to changes in receptors, channels, neurotransmitters, synapses, network connectivity and neurogenesis. Personalization of therapies, in terms of connectivity of the seizure onset zone to the thalamic sub- nucleus and individual characteristics of the seizures, might lead to improved efficacy. Many questions remain about DBS, including the best candidates for different types of neuromodulation, the best targets, the best stimulation parameters, how to minimize side effects and how to deliver current noninvasively. Despite the questions, neuromodulation provides useful new opportunities to treat people with refractory seizures not responding to medicines and not amenable to resective surgery.

Pre-surgical features of intrinsic brain networks predict single and joint epilepsy surgery outcomes

Despite the effectiveness of surgical interventions for the treatment of intractable focal temporal lobe epilepsy (TLE), the substrates that support good outcomes are poorly understood. While algorithms have been developed for the prediction of either seizure or cognitive/psychiatric outcomes alone, no study has reported on the functional and structural architecture that supports joint outcomes. We measured key aspects of pre-surgical whole brain functional/structural network architecture and evaluated their ability to predict post-operative seizure control in combination with cognitive/psychiatric outcomes. Pre-surgically, we identified the intrinsic connectivity networks (ICNs) unique to each person through independent component analysis (ICA), and computed: (1) the spatial-temporal match between each person's ICA components and established, canonical ICNs, (2) the connectivity strength within each identified person-specific ICN, (3) the gray matter (GM) volume underlying the person-specific ICNs, and (4) the amount of variance not explained by the canonical ICNs for each person. Post-surgical seizure control and reliable change indices of change (for language [naming, phonemic fluency], verbal episodic memory, and depression) served as binary outcome responses in random forest (RF) models. The above functional and structural measures served as input predictors. Our empirically derived ICN-based measures customized to the individual showed that good joint seizure and cognitive/psychiatric outcomes depended upon higher levels of brain reserve (GM volume) in specific networks. In contrast, singular outcomes relied on systematic, idiosyncratic variance in the case of seizure control, and the weakened pre-surgical presence of functional ICNs that encompassed the ictal temporal lobe in the case of cognitive/psychiatric outcomes. Our data made clear that the ICNs differed in their propensity to provide reserve for adaptive outcomes, with some providing structural (brain), and others functional (cognitive) reserve. Our customized methodology demonstrated that when substantial unique, patient-specific ICNs are present prior to surgery there is a reliable association with poor post-surgical seizure control. These ICNs are idiosyncratic in that they did not match the canonical, normative ICNs and, therefore, could not be defined functionally, with their location likely varying by patient. This important finding suggested the level of highly individualized ICN's in the epileptic brain may signal the emergence of epileptogenic activity after surgery.

Pro-Ictal State in Human Temporal Lobe Epilepsy

BACKGROUND: Studies of continuous electroencephalography (EEG) suggest that seizures in individuals with focal-onset epilepsies preferentially occur during periods of heightened risk, typified by pathologic brain activities, termed pro-ictal states; however, the presence of (pathologic) pro-ictal states among a plethora of otherwise physiologic (e.g., sleep–wake cycle) states has not been established. METHODS: We studied a prospective, consecutive series of 15 patients with temporal lobe epilepsy who underwent limbic thalamic recordings in addition to routine (cortical) intracranial EEG for seizure localization. For each participant, pro-ictal (45 minutes before seizure onset) and interictal (4 hours removed from all seizures) EEG segments were divided into 10-minute, nonoverlapping windows, which were randomly distributed into training and validation cohorts in a 1:1 ratio. A deep neural classifier was applied to distinguish pro-ictal from interictal brain activities in a patient-specific fashion. RESULTS: We analyzed 1800 patient-hours of continuous thalamocortical EEG. Distinct pro-ictal states were detected in each participant. The median area under the receiver-operating characteristic curve of the classifier was 0.92 (interquartile range, 0.90–0.96). Pro-ictal states were distinguished at least 45 minutes before seizure onset in 13 of 15 participants; in 2 of 15 participants, they were distinguished up to 35 minutes prior. CONCLUSIONS: On the basis of thalamocortical EEG, pro-ictal states — pathologic brain activities during periods of heightened seizure risk — could be identified in patients with temporal lobe epilepsy and were detected, in our small sample, more than one half hour before seizure onset.

Brain network properties of clinical versus subclinical seizures among critically ill children

OBJECTIVE

Electrographic seizures are common among critically ill children, and have been associated with worse outcomes. Despite their often-widespread cortical representation, most of these seizures remain subclinical, a phenomenon which remains poorly understood. We compared the brain network properties of clinical versus subclinical seizures to gain insight into their relative potential deleterious effects.

METHODS

Functional connectivity (phase lag index) and graph measures (global efficiency and clustering coefficients) were computed for 2178 electrographic seizures recorded during 48-hours of 19-channel continuous EEG monitoring obtained in 20 comatose children. Frequency-specific group differences in clinical versus subclinical seizures were analyzed using a non-parametric ANCOVA, adjusting for age, sex, medication exposure, treatment intensity and seizures per subject.

RESULTS

Clinical seizures demonstrated greater functional connectivity than subclinical seizures at alpha frequencies, but less connectivity than subclinical seizures at delta frequencies. Clinical seizures also demonstrated significantly higher median global efficiency than subclinical seizures (p < 0.01), and significantly higher median clustering coefficients across all electrodes at alpha frequencies.

CONCLUSIONS

Clinical expression of seizures correlates with greater alpha synchronization of distributed brain networks.

SIGNIFICANCE

The stronger global and local alpha-mediated functional connectivity observed during clinical seizures may indicate greater pathological network recruitment. These observations motivate further studies to investigate whether the clinical expression of seizures may influence their potential to cause secondary brain injury.

Unruptured brain arteriovenous malformations causing seizures localize to one common brain network

Seizures are a frequent symptom of unruptured brain arteriovenous malformations (bAVMs). However, the brain regions responsible for these seizures remain unclear. To identify the brain regions causally involved in bAVM-related seizures, we retrospectively reviewed 220 patients with unruptured bAVMs. Using voxel-based lesion-symptom mapping (VLSM) analyses, we tested whether individual brain regions were associated with unruptured bAVM-related seizures. The result revealed that unruptured bAVMs causing seizures are anatomically heterogeneous at the voxel level. Subsequently, lesion network mapping (LNM) analyses was performed to determine whether bAVMs causing seizures belonged to a distributed brain network. LNM analyses indicated that these lesions were located in a functional network characterized by connectivity to the left caudate and precuneus. Moreover, the discrimination performance of the identified seizure network was evaluated in discovery set by calculating the individualized network damage score and was tested in validation set. Based on the calculated network damage scores, patients were divided into low-, medium-, and high-risk groups. The prevalence of seizures significantly differed among the three risk categories in both discovery (p = .003) and validation set (p = .004). Finally, we calculated the percentage of voxels in the canonical resting-state networks that overlapped with the seizure-susceptible brain regions to investigate the involvement of resting-state networks. With an involvement percentage over 50%, the frontoparietal control (82.9%), limbic function (76.7%), and default mode network (69.3%) were considered to be impacted in bAVM-related seizures. Our study identified the seizure-susceptible brain regions for unruptured bAVMs, which could be a plausible neuroimaging biomarker in predicting possible seizures.

Extended follow-up after anterior temporal lobectomy demonstrates seizure recurrence 20+ years postsurgery

OBJECTIVE

Anterior temporal lobectomy (ATL) for medication-resistant localized epilepsy results in ablation or reduction of seizures for most patients. However, some individuals who attain an initial extended period of postsurgical seizure freedom will experience a later seizure recurrence. In this study, we examined the prevalence and some risk factors for late recurrence in an ATL cohort with extensive regular follow-up.

METHODS

Included were 449 patients who underwent ATL at Austin Health, Australia, from 1978 to 2008. Postsurgical follow-up was undertaken 2-3 yearly. Seizure recurrence was tested using Kaplan-Meier analysis, log-rank test, and Cox regression. Late recurrence was qualified as a first disabling seizure >2 years postsurgery. We examined risks within the ATL cohort according to broad pathology groups and tested whether late recurrence differed for the ATL cohort compared to patients who had resections outside the temporal lobe (n = 98).

RESULTS

Median post-ATL follow-up was 22 years (range = .1-38.6), 6% were lost to follow-up, and 12% had died. Probabilities for remaining completely seizure-free after surgery were 51% (95% confidence interval [CI] = 53-63) at 2 postoperative years, 36% (95% CI = 32-41) at 10 years, 32% (95% CI = 27-36) at 20 years, and 30% (95% CI = 25-34) at 25 years. Recurrences were reported up to 23 years postoperatively. Late seizures occurred in all major ATL pathology groups, with increased risk in the "normal" and "distant lesion" groups (p ≤ .03). Comparison between the ATL cohort and patients who underwent extratemporal resection demonstrated similar patterns of late recurrence (p = .74).

SIGNIFICANCE

Some first recurrences were very late, reported decades after ATL. Late recurrences were not unique to any broad ATL pathology group and did not differ according to whether resections were ATL or extratemporal. Reports of these events by patients with residual pathology suggest that potentially epileptogenic abnormalities outside the area of resection may be implicated as one of several possible underlying mechanisms.

Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG

Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.

Uncovering the biological basis of control energy: Structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy

Network control theory is increasingly used to profile the brain's energy landscape via simulations of neural dynamics. This approach estimates the control energy required to simulate the activation of brain circuits based on structural connectome measured using diffusion magnetic resonance imaging, thereby quantifying those circuits' energetic efficiency. The biological basis of control energy, however, remains unknown, hampering its further application. To fill this gap, investigating temporal lobe epilepsy as a lesion model, we show that patients require higher control energy to activate the limbic network than healthy volunteers, especially ipsilateral to the seizure focus. The energetic imbalance between ipsilateral and contralateral temporolimbic regions is tracked by asymmetric patterns of glucose metabolism measured using positron emission tomography, which, in turn, may be selectively explained by asymmetric gray matter loss as evidenced in the hippocampus. Our investigation provides the first theoretical framework unifying gray matter integrity, metabolism, and energetic generation of neural dynamics.

The anterior nucleus of the thalamus plays a role in the epileptic network

OBJECTIVES

We investigated both the metabolic differences and interictal/ictal discharges of the anterior nucleus of the thalamus (ANT) in patients with epilepsy to clarify the relationship between the ANT and the epileptic network.

METHODS

Nineteen patients with drug-resistant epilepsy who underwent stereoelectroencephalography were studied. Metabolic differences in ANT were analyzed using [18F] fluorodeoxyglucose-positron emission tomography with three-dimensional (3D) visual and quantitative analyses. Interictal and ictal discharges in the ANT were analyzed using visual and time-frequency analyses. The relationship between interictal discharge and metabolic differences was analyzed.

RESULTS

We found that patients with temporal lobe epilepsy (TLE) showed significant metabolic differences in bilateral ANT compared with extratemporal lobe epilepsy in 3D visual and quantitative analyses. Four types of interictal activities were recorded from the ANT: spike, high-frequency oscillation (HFO), slow-wave, and α-rhythmic activity. Spike and HFO waveforms were recorded mainly in patients with TLE. Two spike patterns were recorded: synchronous and independent. In 83.3% of patients, ANT was involved during seizures. Three seizure onset types of ANT were recorded: low-voltage fast activity, rhythmic spikes, and theta band discharge. The time interval of seizure onset between the seizure onset zone and ANT showed two patterns: immediate and delayed.

INTERPRETATION

ANT can receive either interictal discharges or ictal discharges which propagate from the epileptogenic zones. Independent epileptic discharges can also be recorded from the ANT in some patients. Metabolic anomalies and epileptic discharges in the ANT indicate that the ANT plays a role in the epileptic network in most patients with epilepsy, especially TLE.

Amplitude synchronization of spontaneous activity of medial and lateral temporal gyri reveals altered thalamic connectivity in patients with temporal lobe epilepsy

In this study, we examined whether amplitude synchronization of medial (MTL) and lateral (LTL) temporal lobes can detect unique alterations in patients with MTL epilepsy (mTLE) with mesial temporal sclerosis (MTS). This was a retrospective study of preoperative resting-state fMRI (rsfMRI) data from 31 patients with mTLE with MTS (age 23-69) and 16 controls (age 21-35). fMRI data were preprocessed based on a multistep preprocessing pipeline and registered to a standard space. Using each subject's T1-weighted scan, the MTL and LTL were automatically segmented, manually revised and then fit to a standard space using a symmetric normalization registration algorithm. Dual regression analysis was applied on preprocessed rsfMRI data to detect amplitude synchronization of medial and lateral temporal segments with the rest of the brain. We calculated the overlapped volume ratio of synchronized voxels within specific target regions including the thalamus (total and bilateral). A general linear model was used with Bonferroni correction for covariates of epilepsy duration and age of patient at scan to statistically compare synchronization in patients with mTLE with MTS and controls, as well as with respect to whether patients remained seizure-free (SF) or not (NSF) after receiving epilepsy surgery. We found increased ipsilateral positive connectivity between the LTLs and the thalamus and contralateral negative connectivity between the MTLs and the thalamus in patients with mTLE with MTS compared to controls. We also found increased asymmetry of functional connectivity between temporal lobe subregions and the thalamus in patients with mTLE with MTS, with increased positive connectivity between the LTL and the lesional-side thalamus as well as increased negative connectivity between the MTL and the nonlesional-side thalamus. This asymmetry was also seen in NSF patients but was not seen in SF patients and controls. Amplitude synchronization was an effective method to detect functional connectivity alterations in patients with mTLE with MTS. Patients with mTLE with MTS overall showed increased temporal-thalamic connectivity. There was increased functional involvement of the thalamus in MTS, underscoring its role in seizure spread. Increased functional thalamic asymmetry patterns in NSF patients may have a potential role in prognosticating patient response to surgery. Elucidating regions with altered functional connectivity to temporal regions can improve understanding of the involvement of different regions in the disease to potentially target for intervention or use for prognosis for surgery. Future studies are needed to examine the effectiveness of using patient-specific abnormalities in patterns to predict surgical outcome.

Thalamic stereoelectroencephalography in epilepsy surgery: a scoping literature review

OBJECTIVE

Stereoelectroencephalography (sEEG) is a well-established surgical method for defining the epileptogenic network. Traditionally reserved for identifying discrete cortical regions for resection or ablation, sEEG in current practice is also used for identifying more broadly involved subcortical epileptic network components, driven by the availability of brain-based neuromodulation strategies. In particular, sEEG investigations including thalamic nuclei are becoming more frequent in parallel with the increase in therapeutic strategies involving thalamic targets such as deep brain stimulation (DBS) and responsive neurostimulation (RNS). The objective to this study was to evaluate existing evidence and trends regarding the purpose, techniques, and relevant electrographic findings of thalamic sEEG.

METHODS

MEDLINE and Embase databases were systematically queried for eligible peer-reviewed studies involving sEEG electrode implantation into thalamic nuclei of patients with epilepsy. Available data were abstracted concerning preoperative workup and purpose for implanting the thalamus, thalamic targets and trajectories, and electrophysiological methodology and findings.

RESULTS

sEEG investigations have included thalamic targets for both basic and clinical research purposes. Medial pulvinar, dorsomedial, anterior, and centromedian nuclei have been the most frequently studied. Few studies have reported any complications with thalamic sEEG implantation, and no studies have reported long-term complications. Various methods have been utilized to characterize thalamic activity in epileptic disorders including evoked potentials, power spectrograms, synchronization indices, and the epileptogenicity index. Thalamic intracranial recordings are beginning to be used to guide neuromodulation strategies including RNS and DBS, as well as to understand complex, network-dependent seizure disorders.

CONCLUSIONS

Inclusion of thalamic coverage during sEEG evaluation in drug-resistant epilepsy is a growing practice and is amenable to various methods of electrographic data analysis. Further study is required to establish well-defined criteria for thalamic implantation during invasive investigations as well as safety and ethical considerations.

Towards network-guided neuromodulation for epilepsy

Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.

Recent developments in stereo electroencephalography monitoring for epilepsy surgery

Recently the utilization of the stereo electroencephalography (SEEG) method has exploded globally. It is now the preferred method of intracranial monitoring for epilepsy. Since its inception, the basic tenet of the SEEG method remains the same: strategic implantation of intracerebral electrodes based on a hypothesis grounded on anatomo-electroclinical correlation, interpretation of interictal and ictal abnormalities, and formation of a surgical plan based on these data. However, there are recent advancements in all these domains-electrodes implantations, data interpretation, and therapeutic strategy- that can make the SEEG a more accessible and effective approach. In this narrative review, these newer developments are discussed and summarized. Regarding implantation, efficient commercial robotic systems are now increasingly available, which are also more accurate in implanting electrodes. In terms of ictal and interictal abnormalities, newer studies focused on correlating these abnormalities with pathological substrates and surgical outcomes and analyzing high-frequency oscillations and cortical-subcortical connectivity. These abnormalities can now be further quantified using advanced tools (spectrum, spatiotemporal, connectivity analysis, and machine learning algorithms) for objective and efficient interpretation. Another aspect of recent development is renewed interest in SEEG-based electrical stimulation mapping (ESM). The SEEG-ESM has been used in defining epileptogenic networks, mapping eloquent cortex (primarily language), and analyzing cortico-cortical evoked potential. Regarding SEEG-guided direct therapeutic strategy, several clinical studies evaluated the use of radiofrequency thermocoagulation. As the emerging SEEG-based diagnosis and therapeutics are better evolved, treatments aimed at specific epileptogenic networks without compromising the eloquent cortex will become more easily accessible to improve the lives of individuals with drug-resistant epilepsy (DRE).

The Role of MRI in the Treatment of Drug-Resistant Focal Epilepsy

BACKGROUND

Epilepsy is a prevalent chronic condition affecting about 50 million people worldwide. A third of patients with focal epilepsy suffer from seizures unresponsive to medication. Uncontrolled seizures damage the brain, are associated with cognitive decline, and have negative impact on well-being. For these patients, the surgical resection of the brain region that gives rise to seizures is the most effective treatment.

SUMMARY

Magnetic resonance imaging (MRI) plays a central role in detecting epileptogenic brain lesions. In this review, we critically discuss advances in neuroimaging acquisition, analytical post-acquisition techniques, and machine leaning methods for the detection of epileptogenic lesions, prediction of clinical outcomes, and identification of disease subtypes.

KEY MESSAGE

MRI is a mandatory investigation for diagnosis and treatment of epilepsy, particularly when surgery is being considered. Continuous progress in imaging techniques, combined with machine learning, will continue to push the boundaries of lesion visibility and provide increasingly precise predictors of clinical outcomes. Current efforts aiming at strengthening the competences of epileptologists in neuroimaging will ultimately reduce the need for invasive diagnostics.

Fingerprinting seizure outcome after temporal lobe surgery using preoperative connectomic mapping

This scientific commentary refers to 'Presurgical temporal lobe epilepsy connectome fingerprint for seizure outcome prediction' by Morgan et al. (https://doi.org/10.1093/braincomms/fcac128) in Brain Communications.

Brain structural connectivity sub typing in unilateral temporal lobe epilepsy

To categorize and clinically characterize subtypes of brain structural connectivity patterns in unilateral temporal lobe epilepsy (TLE). Voxel based morphometry (VBM) and surfaced based morphometry (SBM) analysis were used to detect brain structural alterations associated with TLE from MRI data. Principal component analysis (PCA) was performed to identify subtypes of brain structural connectivity patterns. Correlation analysis was used to explore associations between PC scores and clinical characteristics. A total of 59 patients with TLE and 100 healthy adults were included in this study. Widespread cortical atrophy was shown in both left and right TLE (P < 0.05, FWE corrected). Six principal components (PCs) that explained more than 70% of the variance were extracted for left and right TLE, reflecting patterns of brain structural connectivity. PCs representing perisylvian connectivity were positively correlated with verbal IQ (left TLE: r = 0.696, P < 0.001; right TLE: r = 0.484, P = 0.012) and total IQ (left TLE r = 0.608, P < 0.001) and negatively correlated with disease duration (r = -0.448, P = 0.009). In left TLE, the PC in the ipsilateral mesial temporal region was negatively correlated with age at onset (r = -0.382, P = 0.028). In right TLE, the PC representing the default mode network was negatively correlated with number of antiepileptic drugs (r = -0.407, P = 0.039). This study categorized subtypes of unilateral TLE based on brain structural connectivity patterns. Findings may provide insight into seizure pathways, the pathophysiology of epilepsy, including comorbidities such as cognitive impairment, and help predict treatment outcomes.

Characterizing Thalamic and Basal Ganglia Nuclei in Medically Intractable Focal Epilepsy by MR Fingerprinting

OBJECTIVES

Magnetic resonance fingerprinting (MRF) is a novel, quantitative and noninvasive technique to measure brain tissue properties. We aim to use MRF for characterizing normal-appearing thalamic and basal ganglia nuclei in the epileptic brain.

METHODS

A 3D MRF protocol (1mm³ isotropic resolution) was acquired from 48 patients with unilateral medically refractory focal epilepsy and 39 healthy controls (HCs). Whole-brain T1 and T2 maps (containing T1 and T2 relaxation times) were reconstructed for each subject. Ten subcortical nuclei in the thalamus and basal ganglia were segmented as regions of interest (ROIs), within which the mean T1 and T2 values, as well as their coefficient of variation (CV) were compared between the patients and HCs at group level. Subgroup and correlation analyses were performed to examine the relationship between significant MRF measures and various clinical characteristics. Using significantly abnormal MRF measures from the group-level analyses, support vector machine (SVM) and logistic regression machine learning models were built and tested with 5-fold and 10-fold cross-validations, to separate patients from HCs, and to separate patients with left-sided and right-sided epilepsy, at individual level.

RESULTS

MRF revealed increased T1 mean value in the ipsilateral thalamus and nucleus accumbens; increased T1 CV in bilateral thalamus, bilateral pallidum, and ipsilateral caudate; and increased T2 CV in the ipsilateral thalamus in patients compared to HCs (P<0.05, FDR corrected). The SVM classifier produced 78.2% average accuracy to separate individual patients from HCs, with AUC of 0.83. The logistic regression classifier produced 67.4% average accuracy to separate patients with left-sided and right-sided epilepsy, with AUC of 0.72.

SIGNIFICANCE

MRF revealed bilateral tissue-property changes in the normal-appearing thalamus and basal ganglia, with ipsilateral predominance and thalamic preference, suggesting subcortical involvement/impairment in patients with medically intractable focal epilepsy. The individual-level performance of the MRF-based machine-learning models suggests potential opportunities for predicting lateralization.

Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging

OBJECTIVE

We sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality and surgical outcome.

METHODS

151 subjects were included in this analysis: 62 patients (aged 18-68 years, 36 women) and 89 healthy controls (aged 18-71 years, 47 women). We created a supervised machine learning technique that uses diffusion-weighted metrics to classify subject groups. Specifically, we sought to classify patients versus healthy controls, unilateral versus bilateral temporal lobe epilepsy, left versus right temporal lobe epilepsy and seizure-free versus not seizure-free surgical outcome. We then reduced the dimensionality of derived features with community detection for ease of interpretation.

RESULTS

We classified the subject groups in withheld testing data sets with a cross-fold average testing areas under the receiver operating characteristic curve of 0.745 for patients versus healthy controls, 1.000 for unilateral versus bilateral seizure onset, 0.662 for left versus right seizure onset, 0.800 for left-sided seizure-free vsersu not seizure-free surgical outcome and 0.775 for right-sided seizure-free versus not seizure-free surgical outcome.

CONCLUSIONS

This technique classifies important clinical decisions in the presurgical workup of temporal lobe epilepsy by generating discerning white-matter features. We believe that this work augments existing network connectivity findings in the field by further elucidating important white-matter pathology in temporal lobe epilepsy. We hope that this work contributes to recent efforts aimed at using diffusion imaging as an augmentation to the presurgical workup of this devastating neurological disorder.

Presurgical temporal lobe epilepsy connectome fingerprint for seizure outcome prediction

Temporal lobe epilepsy presents a unique situation where confident clinical localization of the seizure focus does not always result in a seizure-free or favourable outcome after mesial temporal surgery. In this work, magnetic resonance imaging derived functional and structural whole-brain connectivity was used to compute a network fingerprint that captures the connectivity profile characteristics that are common across a group of nine of these patients with seizure-free outcome. The connectivity profile was then computed for 38 left-out patients with the hypothesis that similarity to the fingerprint indicates seizure-free surgical outcome. Patient profile distance to the fingerprint was compared with 1-year seizure outcome and standard clinical parameters. Distance to the fingerprint was higher for patients with Engel III-IV 1-year outcome compared with those with Engel Ia, Ib-d, and II outcome (Kruskal-Wallis, P < 0.01; Wilcoxon rank-sum p corr <0.05 Bonferroni-corrected). Receiver operator characteristic analysis revealed 100% sensitivity and 90% specificity in identifying patients with Engel III-IV outcome based on distance to the fingerprint in the left-out patients. Furthermore, distance to the fingerprint was not related to any individual clinical parameter including age at scan, duration of disease, total seizure frequency, presence of mesial temporal sclerosis, lateralizing ictal, interictal scalp electroencephalography, invasive stereo-encephalography, or positron emission tomography. And two published algorithms utilizing multiple clinical measures for predicting seizure outcome were not related to distance to the fingerprint, nor predictive of seizure outcome in this cohort. The functional and structural connectome fingerprint provides quantitative, clinically interpretable and significant information not captured by standard clinical assessments alone or in combinations. This automated and simple method may improve patient-specific prediction of seizure outcome in patients with a clinically identified focus in the mesial temporal lobe.

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

Drug resistant epilepsy is a disorder involving widespread brain network alterations. Recently, many groups have reported neuroimaging and electrophysiology network analysis techniques to aid medical management, support presurgical planning, and understand postsurgical seizure persistence. While these approaches may supplement standard tests to improve care, they are not yet used clinically or influencing medical or surgical decisions. When will this change? Which approaches have shown the most promise? What are the barriers to translating them into clinical use? How do we facilitate this transition? In this review, we will discuss progress, barriers, and next steps regarding the integration of brain network analysis into the medical and presurgical pipeline.

Magnetoencephalography Imaging Reveals Abnormal Information Flow in Temporal Lobe Epilepsy

Background/Introduction: Widespread network disruption has been hypothesized to be an important predictor of outcomes in patients with refractory temporal lobe epilepsy (TLE). Most studies examining functional network disruption in epilepsy have largely focused on the symmetric bidirectional metrics of the strength of network connections. However, a more complete description of network dysfunction impacts in epilepsy requires an investigation of the potentially more sensitive directional metrics of information flow. Methods: This study describes a whole-brain magnetoencephalography-imaging approach to examine resting-state directional information flow networks, quantified by phase-transfer entropy (PTE), in patients with TLE compared with healthy controls (HCs). Associations between PTE and clinical characteristics of epilepsy syndrome are also investigated. Results: Deficits of information flow were specific to alpha-band frequencies. In alpha band, while HCs exhibit a clear posterior-to-anterior directionality of information flow, in patients with TLE, this pattern of regional information outflow and inflow was significantly altered in the frontal and occipital regions. The changes in information flow within the alpha band in selected brain regions were correlated with interictal spike frequency and duration of epilepsy. Conclusions: Impaired information flow is an important dimension of network dysfunction associated with the pathophysiological mechanisms of TLE.

Neuroimaging and thalamic connectomics in epilepsy neuromodulation

Neuromodulation is an increasingly utilized therapy for the treatment of people with drug-resistant epilepsy. To date, the most common and effective target has been the thalamus, which is known to play a key role in multiple forms of epilepsy. Neuroimaging has facilitated rapid developments in the understanding of functional targets, surgical and programming techniques, and the effects of thalamic stimulation. In this review, the role of neuroimaging in neuromodulation is explored. First, the structural and functional changes of the thalamus in common epilepsy syndromes are discussed as the rationale for neuromodulation of the thalamus. Next, methods for imaging different thalamic nuclei are presented, as well as rationale for the need of direct surgical targeting rather than reliance on traditional stereotactic coordinates. Lastly, we discuss the potential role of neuroimaging in assessing the effects of thalamic stimulation and as a potential biomarker for neuromodulation outcomes.

Altered Temporal Variations of Functional Connectivity Associated With Surgical Outcomes in Drug-Resistant Temporal Lobe Epilepsy

Background

Currently, more than one-third of patients with drug-resistant temporal lobe epilepsy (TLE) continue to develop seizures after resection surgery. Dynamic functional network connectivity (DFNC) analyses, capturing temporal properties of functional connectivity during MRI acquisition, may help us identify unfavorable surgical outcomes. The purpose of this work was to explore the association of DFNC variations of preoperative resting-state MRI and surgical outcomes in patients with drug-resistant TLE.

Methods

We evaluated 61 patients with TLE matched for age and gender with 51 healthy controls (HC). Patients with TLE were classified as seizure-free (n = 39) and not seizure-free (n = 16) based on the Engel surgical outcome scale. Six patients were unable to confirm the postoperative status and were not included in the subgroup analysis. The DFNC was calculated using group spatial independent component analysis and the sliding window approach.

Results

Dynamic functional network connectivity analyses suggested two distinct connectivity “States.” The dynamic connectivity state of patients with TLE was different from HC. TLE subgroup analyses showed not seizure-free (NSF) patients spent significantly more time in State II compared to seizure-free (SF) patients and HC. Further, the number of transitions from State II to State I was significantly lower in NSF patients. SF patients had compensatory enhancement of DFNC strengths between default and dorsal attention network, as well as within the default network. While reduced DFNC strengths of within-network and inter-network were both observed in NSF patients, patients with abnormally temporal properties and more extension DFNC strength alterations were less likely to receive seizure freedom.

Conclusions

Our study indicates that DFNC could offer a better understanding of dynamic neural impairment mechanisms of drug-resistant TLE functional network, epileptic brain network reorganization, and provide an additional preoperative evaluation support for surgical treatment of drug-resistant TLE.

Neuroimaging gradient alterations and epileptogenic prediction in focal cortical dysplasia Ⅲa

INTRODUCTION

Focal cortical dysplasia Type Ⅲa (FCD Ⅲa) is a highly prevalent temporal lobe epilepsy but the seizure outcomes are not satisfactory after epilepsy surgery. Hence, quantitative neuroimaging, epileptogenic alterations, as well as their values in guiding surgery are worth exploring.

METHODS

We examined 69 patients with pathologically verified FCD Ⅲa using multimodal neuroimaging and stereoelectroencephalography (SEEG). Among them, 18 received postoperative imaging which showed the extent of surgical resection and 9 underwent SEEG implantation. We also explored neuroimaging gradient alterations along with the distance to the temporal pole. Subsequently, the machine learning regression model was employed to predict whole-brain epileptogenicity. Lastly, the correlation between neuroimaging or epileptogenicity and surgical cavities was assessed.

RESULTS

FCD Ⅲa displayed neuroimaging gradient alterations on the temporal neocortex, morphology-signal intensity decoupling, low similarity of intra-morphological features and high similarity of intra-signal intensity features. The support vector regression model was successfully applied at the whole-brain level to calculate the continuous epileptogenic value at each vertex (mean-squared error = 13.8 ± 9.8).

CONCLUSION

Our study investigated the neuroimaging gradient alterations and epileptogenicity of FCD Ⅲa, along with their potential values in guiding suitable resection range and in predicting postoperative seizure outcomes. The conclusions from this study may facilitate an accurate presurgical examination of FCD Ⅲa. However, further investigation including a larger cohort is necessary to confirm the results.

Common functional connectivity alterations in focal epilepsies identified by machine learning

OBJECTIVE

This study was undertaken to identify shared functional network characteristics among focal epilepsies of different etiologies, to distinguish epilepsy patients from controls, and to lateralize seizure focus using functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (MRI).

METHODS

Data were taken from 103 adult and 65 pediatric focal epilepsy patients (with or without lesion on MRI) and 109 controls across four epilepsy centers. We used three whole-brain FC measures: parcelwise connectivity matrix, mean FC, and degree of FC. We trained support vector machine models with fivefold cross-validation (1) to distinguish patients from controls and (2) to lateralize the hemisphere of seizure onset in patients. We reported the regions and connections with the highest importance from each model as the common FC differences between the compared groups.

RESULTS

FC measures related to the default mode and limbic networks had higher importance relative to other networks for distinguishing epilepsy patients from controls. In lateralization models, regions related to somatosensory, visual, default mode, and basal ganglia showed higher importance. The epilepsy versus control classification model trained using a 400-parcel connectivity matrix achieved a median testing accuracy of 75.6% (median area under the curve [AUC] = .83) in repeated independent testing. Lateralization accuracy using the 400-parcel connectivity matrix reached a median accuracy of 64.0% (median AUC = .69).

SIGNIFICANCE

Machine learning models revealed common FC alterations in a heterogeneous group of patients with focal epilepsies. The distribution of the most altered regions supports the hypothesis that shared functional alteration exists beyond the seizure onset zone and its epileptic network. We showed that FC measures can distinguish patients from controls, and further lateralize focal epilepsies. Future studies are needed to confirm these findings by using larger numbers of epilepsy patients.

Characterization of Hippocampal-Thalamic-Cortical Morphometric Reorganization in Temporal Lobe Epilepsy

Introduction

Brain cortico-subcortical connectivity has been investigated in epilepsy using the functional MRI (MRI). Although structural images cannot demonstrate dynamic changes, they provide higher spatial resolution, which allows exploration of the organization of brain in greater detail.

Methods

We used high-resolution brain MRI to study the hippocampal-thalamic-cortical networks in temporal lobe epilepsy (TLE) using a volume-based morphometric method. We enrolled 22 right-TLE, 33 left-TLE, and 28 age/gender-matched controls retrospectively. FreeSurfer software was used for the thalamus segmentation.

Results

Among the 50 subfields, ipsilateral anterior, lateral, and parts of the intralaminar and medial nuclei, as well as the contralateral parts of lateral nuclei had significant volume loss in both TLE. The anteroventral nucleus was most vulnerable. Most thalamic subfields were susceptible to seizure burden, especially the left-TLE. SPM12 was used to conduct an analysis of the gray matter density (GMD) maps. Decreased extratemporal GMD occurred bilaterally. Both TLE demonstrated significant GMD loss over the ipsilateral inferior frontal gyrus, precentral gyrus, and medial orbital cortices.

Significance

Thalamic subfield atrophy was related to the ipsilateral inferior frontal GMD changes, which presented positively in left-TLE and negatively in right-TLE. These findings suggest prefrontal-thalamo-hippocampal network disruption in TLE.

Individual [18F]FDG PET and functional MRI based on simultaneous PET/MRI may predict seizure recurrence after temporal lobe epilepsy surgery

OBJECTIVES

To investigate the individual measures of brain glucose metabolism, neural activity obtained from simultaneous ¹⁸[F]FDG PET/MRI, and their association with surgical outcomes in medial temporal lobe epilepsy due to hippocampal sclerosis (mTLE-HS).

METHODS

Thirty-nine unilateral mTLE-HS patients who underwent anterior temporal lobectomy were classified as having completely seizure-free (Engel class IA; n = 22) or non-seizure-free (Engel class IB-IV; n = 17) outcomes at 1 year after surgery. Preoperative [¹⁸F]FDG PET and functional MRI (fMRI) were obtained from a simultaneous PET/MRI scanner, and individual glucose metabolism and fractional amplitude of low-frequency fluctuation (fALFF) were evaluated by standardizing these with respect to healthy controls. These abnormality measures and clinical data from each patient were incorporated into a machine learning framework (gradient boosting decision tree and logistic regression analysis) to estimate seizure recurrence. The predictive values of features were evaluated by the receiver operating characteristic (ROC) curve in the training and test cohorts.

RESULTS

The machine learning classification model showed [¹⁸F]FDG PET and fMRI variations in contralateral hippocampal network and age of onset identify unfavorable surgical outcomes effectively. In the validation dataset, the logistic regression model with [¹⁸F]FDG PET and fALFF obtained from simultaneous [¹⁸F]FDG PET/MRI gained the maximum area under the ROC curve of 0.905 for seizure recurrence, higher than 0.762 with ¹⁸[F]-FDG PET, and 0.810 with fALFF alone.

CONCLUSION

Machine learning model suggests individual [¹⁸F]FDG PET and fMRI variations in contralateral hippocampal network based on ¹⁸[F]-FDG PET/MRI could serve as a potential biomarker of unfavorable surgical outcomes.

KEY POINTS

• Individual [¹⁸F]FDG PET and fMRI obtained from preoperative [¹⁸F]FDG PET/MR were investigated. • Individual differences were further assessed based on a seizure propagation network. • Machine learning can classify surgical outcomes with 90.5% accuracy.