The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy

Risk Factors for Seizure Worsening After Epilepsy Surgery in Children and Adults: A Population-Based Register Study

BACKGROUND

Increased seizure frequency and new-onset tonic-clonic seizures (TCS) have been reported after epilepsy surgery.

OBJECTIVE

To analyze potential risk factors for these outcomes in a large cohort.

METHODS

We studied prospectively collected data in the Swedish National Epilepsy Surgery Register on increased seizure frequency and new-onset TCS after epilepsy surgery 1990-2015.

RESULTS

Two-year seizure outcome was available for 1407 procedures, and data on seizure types for 1372. Increased seizure frequency at follow-up compared to baseline occurred in 56 cases (4.0%) and new-onset TCS in 53 (3.9%; 6.6% of the patients without preoperative TCS). Increased frequency was more common in reoperations compared to first surgeries (7.9% vs 3.1%; P = .001) and so too for new-onset TCS (6.7% vs 3.2%; P = .017). For first surgeries, binary logistic regression was used to analyze predictors for each outcome. In univariable analysis, significant predictors for increased seizure frequency were lower age of onset, lower age at surgery, shorter epilepsy duration, preoperative neurological deficit, intellectual disability, high preoperative seizure frequency, and extratemporal procedures. For new-onset TCS, significant predictors were preoperative deficit, intellectual disability, and nonresective procedures. In multivariable analysis, independent predictors for increased seizure frequency were lower age at surgery (odds ratio (OR) 0.70 per increasing 10-yr interval, 95% CI 0.53-0.93), type of surgery (OR 0.42 for temporal lobe resections compared to other procedures, 95% CI 0.19-0.92), and for new-onset TCS preoperative neurological deficit (OR 2.57, 95% CI 1.32-5.01).

CONCLUSION

Seizure worsening is rare but should be discussed when counseling patients. The identified risk factors may assist informed decision-making before surgery.

Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy

OBJECTIVE

Our objective was to identify whether the whole-brain structural network alterations in patients with temporal lobe epilepsy (TLE) and focal to bilateral tonic-clonic seizures (FBTCS) differ from alterations in patients without FBTCS.

METHODS

We dichotomized a cohort of 83 drug-resistant patients with TLE into those with and without FBTCS and compared each group to 29 healthy controls. For each subject, we used diffusion-weighted magnetic resonance imaging to construct whole-brain structural networks. First, we measured the extent of alterations by performing FBTCS-negative (FBTCS-) versus control and FBTCS-positive (FBTCS+) versus control comparisons, thereby delineating altered subnetworks of the whole-brain structural network. Second, by standardizing each patient's networks using control networks, we measured the subject-specific abnormality at every brain region in the network, thereby quantifying the spatial localization and the amount of abnormality in every patient.

RESULTS

Both FBTCS+ and FBTCS- patient groups had altered subnetworks with reduced fractional anisotropy and increased mean diffusivity compared to controls. The altered subnetwork in FBTCS+ patients was more widespread than in FBTCS- patients (441 connections altered at t > 3, p < .001 in FBTCS+ compared to 21 connections altered at t > 3, p = .01 in FBTCS-). Significantly greater abnormalities-aggregated over the entire brain network as well as assessed at the resolution of individual brain areas-were present in FBTCS+ patients (p < .001, d = .82, 95% confidence interval = .32-1.3). In contrast, the fewer abnormalities present in FBTCS- patients were mainly localized to the temporal and frontal areas.

SIGNIFICANCE

The whole-brain structural network is altered to a greater and more widespread extent in patients with TLE and FBTCS. We suggest that these abnormal networks may serve as an underlying structural basis or consequence of the greater seizure spread observed in FBTCS.

Altered structural connectome in non-lesional newly diagnosed focal epilepsy: Relation to pharmacoresistance

Despite an expanding literature on brain alterations in patients with longstanding epilepsy, few neuroimaging studies investigate patients with newly diagnosed focal epilepsy (NDfE). Understanding brain network impairments at diagnosis is necessary to elucidate whether or not brain abnormalities are principally due to the chronicity of the disorder and to develop prognostic markers of treatment outcome. Most adults with NDfE do not have MRI-identifiable lesions and the reasons for seizure onset and refractoriness are unknown. We applied structural connectomics to T1-weighted and multi-shell diffusion MRI data with generalized q-sampling image reconstruction using Network Based Statistics (NBS). We scanned 27 patients within an average of 3.7 (SD = 2.9) months of diagnosis and anti-epileptic drug treatment outcomes were collected 24 months after diagnosis. Seven patients were excluded due to lesional NDfE and outcome data was available in 17 patients. Compared to 29 healthy controls, patients with non-lesional NDfE had connectomes with significantly decreased quantitative anisotropy in edges connecting right temporal, frontal and thalamic nodes and increased diffusivity in edges between bilateral temporal, frontal, occipital and parietal nodes. Compared to controls, patients with persistent seizures showed the largest effect size (|d|>=1) for decreased anisotropy in right parietal edges and increased diffusivity in edges between left thalamus and left parietal nodes. Compared to controls, patients who were rendered seizure-free showed the largest effect size for decreased anisotropy in the edge connecting the left thalamus and right temporal nodes and increased diffusivity in edges connecting right frontal nodes. As demonstrated by large effect sizes, connectomes with decreased anisotropy (edge between right frontal and left insular nodes) and increased diffusivity (edge between right thalamus and left parietal nodes) were found in patients with persistent seizures compared to patients who became seizure-free. Patients who had persistent seizures showed larger effect sizes in all network metrics than patients who became seizure-free when compared to each other and compared to controls. Furthermore, patients with focal-to-bilateral tonic-clonic seizures (FBTCS, N = 11) had decreased quantitative anisotropy in a bilateral network involving edges between temporal, parietal and frontal nodes with greater effect sizes than those of patients without FBTCS (N = 9). NBS findings between patients and controls indicated that structural network changes are not necessarily a consequence of longstanding refractory epilepsy and instead are present at the time of diagnosis. Computed effect sizes suggest that there may be structural network MRI-markers of future pharmacoresistance and seizure severity in patients with a new diagnosis of focal epilepsy.

Lateralizing magnetic resonance imaging findings in mesial temporal sclerosis and correlation with seizure and neurocognitive outcome after temporal lobectomy

BACKGROUND

Mesial temporal sclerosis (MTS) is the most common cause of temporal lobe epilepsy (TLE). While MTS is associated with a high cure rate after temporal lobectomy (TL), postoperative neurocognitive deficits are common, and a subset of patients may continue to have refractory seizures.

OBJECTIVE

To use magnetic resonance (MR) volumetry to identify features of the mesial temporal lobe in patients with MTS that correlate with seizure and neurocognitive outcome after temporal lobectomy.

METHODS

Thirty-five patients with unilateral MTS, high-resolution MR imaging, and at least one year of postoperative assessments were retrospectively examined. Volumetric analysis of the hippocampus, parahippocampal gyrus (PHG) and FLAIR hyperintensity of the affected temporal lobe was performed. TL resections were manually segmented, and resection heat maps reflecting seizure outcome were produced. The degree of preoperative atrophy of the affected mesial structures relative to the unaffected side were related to preoperative and postoperative component scores of verbal and visuospatial memory as well as confrontation naming.

RESULTS

Greater FLAIR hyperintense volume was associated with favorable seizure outcome at one year and last follow-up. Resections extending most medial and posteriorly were associated with favorable seizure outcome. In patients with left MTS, less atrophy of the affected PHG was predictive of higher preoperative naming scores and greater postoperative naming deficit, while less hippocampal atrophy was predictive of higher preoperative verbal memory component scores.

CONCLUSION

Greater hippocampal FLAIR volume is associated with favorable surgical outcome. Hippocampal volume correlates with preoperative verbal memory, while PHG volume is implicated in confrontation naming ability.

Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery

OBJECTIVE

We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure recurrences.

METHODS

We examined data from 51 patients with TLE who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the preoperative structural, diffusion, and postoperative structural MRI, we generated 2 networks: presurgery network and surgically spared network. Standardizing these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient into a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.

RESULTS

Patients with more abnormal nodes had a lower chance of complete seizure freedom at 1 year and, even if seizure-free at 1 year, were more likely to relapse within 5 years. The number of abnormal nodes was greater and their locations more widespread in the surgically spared networks of patients with poor outcome than in patients with good outcome. We achieved an area under the curve of 0.84 ± 0.06 and specificity of 0.89 ± 0.09 in predicting unsuccessful seizure outcomes (International League Against Epilepsy [ILAE] 3-5) as opposed to complete seizure freedom (ILAE 1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year 1 and associated with relapses up to 5 years after surgery.

CONCLUSION

Node abnormality offers a personalized, noninvasive marker that can be combined with clinical data to better estimate the chances of seizure freedom at 1 year and subsequent relapse up to 5 years after ATLR.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that node abnormality predicts postsurgical seizure recurrence.

Connectome biomarkers of drug-resistant epilepsy

Drug-resistant epilepsy (DRE) considerably affects patient health, cognition, and well-being, and disproportionally contributes to the overall burden of epilepsy. The most common DRE syndromes are temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal epilepsy related to cortical malformations. Both syndromes have been traditionally considered as "focal," and most patients benefit from brain surgery for long-term seizure control. However, increasing evidence indicates that many DRE patients also present with widespread structural and functional network disruptions. These anomalies have been suggested to relate to cognitive impairment and prognosis, highlighting their importance for patient management. The advent of multimodal neuroimaging and formal methods to quantify complex systems has offered unprecedented ability to profile structural and functional brain networks in DRE patients. Here, we performed a systematic review on existing DRE network biomarker candidates and their contribution to three key application areas: (1) modeling of cognitive impairments, (2) localization of the surgical target, and (3) prediction of clinical and cognitive outcomes after surgery. Although network biomarkers hold promise for a range of clinical applications, translation of neuroimaging biomarkers to the patient's bedside has been challenged by a lack of clinical and prospective studies. We therefore close by highlighting conceptual and methodological strategies to improve the evaluation and accessibility of network biomarkers, and ultimately guide clinically actionable decisions.

Thalamohippocampal atrophy in focal epilepsy of unknown cause at the time of diagnosis

BACKGROUND AND PURPOSE

Patients with chronic focal epilepsy may have atrophy of brain structures important for the generation and maintenance of seizures. However, little research has been conducted in patients with newly diagnosed focal epilepsy (NDfE), despite it being a crucial point in time for understanding the underlying biology of the disorder. We aimed to determine whether patients with NDfE show evidence of volumetric abnormalities of subcortical structures.

METHODS

Eighty-two patients with NDfE and 40 healthy controls underwent magnetic resonance imaging scanning using a standard clinical protocol. Volume estimation of the left and right hippocampus, thalamus, caudate nucleus, putamen and cerebral hemisphere was performed for all participants and normalised to whole brain volume. Volumes lower than two standard deviations below the control mean were considered abnormal. Volumes were analysed with respect to patient clinical characteristics, including treatment outcome 12 months after diagnosis.

RESULTS

Volume of the left hippocampus (p_(FDR-corr)  = 0.04) and left (p_(FDR-corr)  = 0.002) and right (p_(FDR-corr)  = 0.04) thalamus was significantly smaller in patients relative to controls. Relative to the normal volume limits in controls, 11% patients had left hippocampal atrophy, 17% had left thalamic atrophy and 9% had right thalamic atrophy. We did not find evidence of a relationship between volumes and future seizure control or with other clinical characteristics of epilepsy.

CONCLUSIONS

Volumetric abnormalities of structures known to be important for the generation and maintenance of focal seizures are established at the time of epilepsy diagnosis and are not necessarily a result of the chronicity of the disorder.

Topological alterations in older adults with temporal lobe epilepsy are distinct from amnestic mild cognitive impairment

Epilepsy incidence and prevalence peaks in older adults, yet systematic studies of brain aging and epilepsy remain limited. We investigated topological network disruption in older adults with temporal lobe epilepsy (TLE; age > 55 years). Additionally, we examined the potential network disruption overlap between TLE and amnestic mild cognitive impairment (aMCI), the prodromal stage of Alzheimer disease. Measures of network integration ("global path efficiency") and segregation ("transitivity" and "modularity") were calculated from cortical thickness covariance from 73 TLE subjects, 79 aMCI subjects, and 70 healthy controls. Compared to controls, TLE patients demonstrated abnormal measures of segregation (increased transitivity and decreased modularity) and integration (decreased global path efficiency). aMCI patients also displayed increased transitivity and decreased global path efficiency, but these differences were less pronounced than in TLE. At the local level, TLE patients demonstrated decreased local path efficiency focused in the bilateral temporal lobes, whereas aMCI patients had a more frontal-parietal distribution. These results suggest that network disruption at the global and local level is present in both disorders, but global disruption may be a particularly salient feature in older adults with TLE. These findings motivate further research into whether these network changes have distinct cognitive correlates or are progressive in older adults with epilepsy.

Contributions of Imaging to Neuromodulatory Treatment of Drug-Refractory Epilepsy

Epilepsy affects about 1% of the world's population, and up to 30% of all patients will ultimately not achieve freedom from seizures with anticonvulsive medication alone. While surgical resection of a magnetic resonance imaging (MRI) -identifiable lesion remains the first-line treatment option for drug-refractory epilepsy, surgery cannot be offered to all. Neuromodulatory therapy targeting "seizures" instead of "epilepsy" has emerged as a valuable treatment option for these patients, including invasive procedures such as deep brain stimulation (DBS), responsive neurostimulation (RNS) and peripheral approaches such as vagus nerve stimulation (VNS). The purpose of this review is to provide in-depth information on current concepts and evidence on network-level aspects of drug-refractory epilepsy. We reviewed the current evidence gained from studies utilizing advanced imaging methodology, with a specific focus on their contributions to neuromodulatory therapy.

Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom

Objective: To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. Methods: We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. Results: Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, p = 0.01) consistently across three randomly chosen time segments. Conclusion: Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom.

Characterization of postsurgical functional connectivity changes in temporal lobe epilepsy

OBJECTIVE

Seizure outcome after mesial temporal lobe epilepsy (mTLE) surgery is complex and diverse, even across patients with homogeneous presurgical clinical profiles. The authors hypothesized that this is due in part to variations in network connectivity across the brain before and after surgery. Although presurgical network connectivity has been previously characterized in these patients, the objective of this study was to characterize presurgical to postsurgical functional network connectivity changes across the brain after mTLE surgery.

METHODS

Twenty patients with drug-refractory unilateral mTLE (5 left side, 10 female, age 39.3 ± 13.5 years) who underwent either selective amygdalohippocampectomy (n = 13) or temporal lobectomy (n = 7) were included in the study. Presurgical and postsurgical (36.6 ± 14.3 months after surgery) functional connectivity (FC) was measured with 3-T MRI and compared with findings in age-matched healthy controls (n = 44, 21 female, age 39.3 ± 14.3 years). Postsurgical connectivity changes were then related to seizure outcome, type of surgery, and presurgical disease parameters.

RESULTS

The results demonstrated significant decreases of FC from control group values across the brain after surgery that were not present before surgery, including many contralateral hippocampal connections distal to the surgical site. Postsurgical impairment of contralateral precuneus to ipsilateral occipital connectivity was associated with seizure recurrence. Presurgical impairment of the contralateral precuneus to contralateral temporal lobe connectivity was associated with those who underwent selective amygdalohippocampectomy compared to those who had temporal lobectomy. Finally, changes in thalamic connectivity after surgery were linearly related to duration of epilepsy and frequency of consciousness-impairing seizures prior to surgery.

CONCLUSIONS

The widespread contralateral hippocampal FC changes after surgery may be a reflection of an ongoing epileptogenic progression that has been altered by the surgery, rather than a direct result of the surgery itself. This network evolution may contribute to long-term seizure outcome. Therefore, the combination of presurgical network mapping with the understanding of the dynamic effects of surgery on the networks may ultimately be used to create predictors of the likelihood of long-term seizure recurrence in individual patients after mTLE surgery.

White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA-Epilepsy study

The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P < 0.001). Across 'all epilepsies' lower fractional anisotropy was observed in most fibre tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. There were also less robust increases in mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Individuals with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced reductions in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and increased mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of diffusion abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibres in a large multicentre study of epilepsy. Overall, patients with epilepsy showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding more detailed insights into pathological substrates that may explain cognitive and psychiatric co-morbidities and be used to guide biomarker studies of treatment outcomes and/or genetic research.

Network reorganisation following anterior temporal lobe resection and relation with post-surgery seizure relapse: A longitudinal study

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Diffusion changes assessed at two time points following epilepsy surgery.

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Graph theory and connectometry revealed substantial longitudinal diffusion changes.

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Changes were found beyond the site of resection.

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Postoperative seizure freedom associated with longitudinal structural changes.

Objective

To characterise temporal lobe epilepsy (TLE) surgery-induced changes in brain network properties, as measured using diffusion weighted MRI, and investigate their association with postoperative seizure-freedom.

Methods

For 48 patients who underwent anterior temporal lobe resection, diffusion weighted MRI was acquired pre-operatively, 3–4 months post-operatively (N = 48), and again 12 months post-operatively (N = 13). Data for 17 controls were also acquired over the same period. After registering all subjects to a common space, we performed two complementary analyses of the subjects’ quantitative anisotropy (QA) maps. 1) A connectometry analysis which is sensitive to changes in subsections of fasciculi. 2) A graph theory approach which integrates connectivity information across the wider brain network.

Results

We found significant postoperative alterations in QA in patients relative to controls measured over the same period. Reductions were primarily located in the uncinate fasciculus and inferior fronto-occipital fasciculus ipsilaterally for all patients. Larger reductions were associated with postoperative seizure-freedom in left TLE. Increased QA was mainly located in corona radiata and corticopontine tracts. Graph theoretic analysis revealed widespread increases in nodal betweenness centrality, which were not associated with patient outcomes.

Conclusion

Substantial alterations in QA occur in the months after epilepsy surgery, suggesting Wallerian degeneration and strengthening of specific white matter tracts. Greater reductions in QA were related to postoperative seizure freedom in left TLE.

Clinical utility of structural connectomics in predicting memory in temporal lobe epilepsy

OBJECTIVE

To determine the predictive power of white matter neuronal networks (i.e., structural connectomes [SCs]) in discriminating memory-impaired patients with temporal lobe epilepsy (TLE) from those with normal memory.

METHODS

T1- and diffusion MRI (dMRI), clinical variables, and neuropsychological measures of verbal memory were available for 81 patients with TLE. Prediction of memory impairment was performed with a tree-based classifier (XGBoost) for 4 models: (1) a clinical model including demographic and clinical features, (2) a hippocampal volume (HCV) model, (3) a tract model including 5 temporal lobe white matter association tracts derived from a dMRI atlas, and (4) an SC model based on dMRI. SCs were derived by extracting cortical-cortical connections from a temporal lobe subnetwork with probabilistic tractography. Principal component (PC) analysis was then applied to reduce the dimensionality of the SC, yielding 10 PCs. Multimodal models were also tested combining SCs and tracts with HCV. Each model was trained on 48 patients from 1 epilepsy center and tested on 33 patients from a different center.

RESULTS

Multimodal models that included the SC + HCV model yielded the highest classification accuracy (81%; 0.90 sensitivity; 0.67 specificity), outperforming the clinical model (61%; p < 0.001) and HCV model (66%; p < 0.001). In addition, the unimodal SC model (76% accuracy) and tract model (73% accuracy) outperformed the clinical model (p < 0.001) and HCV model (p < 0.001) for classifying patients with TLE with and without memory impairment. Furthermore, the SC identified that short-range temporal-temporal connections were important contributors to memory performance.

CONCLUSION

SCs and tract-based models are stronger predictors of memory impairment in TLE than HCVs and clinical variables. However, SCs may provide additional information about local cortical-cortical connectivity contributing to memory that is not captured in large association tracts.

The ENIGMA-Epilepsy working group: Mapping disease from large data sets

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.

Divergent network properties that predict early surgical failure versus late recurrence in temporal lobe epilepsy

OBJECTIVE

The objectives of this study were to identify functional and structural network properties that are associated with early versus long-term seizure outcomes after mesial temporal lobe epilepsy (mTLE) surgery and to determine how these compare to current clinically used methods for seizure outcome prediction.

METHODS

In this case-control study, 26 presurgical mTLE patients and 44 healthy controls were enrolled to undergo 3-T MRI for functional and structural connectivity mapping across an 8-region network of mTLE seizure propagation, including the hippocampus (left and right), insula (left and right), thalamus (left and right), one midline precuneus, and one midline mid-cingulate. Seizure outcome was assessed annually for up to 3 years. Network properties and current outcome prediction methods related to early and long-term seizure outcome were investigated.

RESULTS

A network model was previously identified across 8 patients with seizure-free mTLE. Results confirmed that whole-network propagation connectivity patterns inconsistent with the mTLE model predict early surgical failure. In those patients with networks consistent with the mTLE network, specific bilateral within-network hippocampal to precuneus impairment (rather than unilateral impairment ipsilateral to the seizure focus) was associated with mild seizure recurrence. No currently used clinical variables offered the same ability to predict long-term outcome.

CONCLUSIONS

It is known that there are important clinical differences between early surgical failure that lead to frequent disabling seizures and late recurrence of less frequent mild seizures. This study demonstrated that divergent network connectivity variability, whole-network versus within-network properties, were uniquely associated with these disparate outcomes.

Functional Networks in Epilepsy Presurgical Evaluation

Continuing advancements in neuroimaging methodology allow for increasingly detailed in vivo characterization of structural and functional brain networks, leading to the recognition of epilepsy as a disorder of large-scale networks. In surgical candidates, analysis of functional networks has proved invaluable for the identification of eloquent brain areas, such as hemispherical language dominance. More recently, connectome-based biomarkers have demonstrated potential to further inform clinical decision making in drug-refractory epilepsy. This article summarizes current evidence on epilepsy as a network disorder, emphasizing potential benefits of network analysis techniques for preoperative assessments and resection planning.

Hippocampal microstructural architecture and surgical outcome: Hippocampal diffusivity could predict seizure relapse

PURPOSE

Our aim was to study the microstructural architecture of the contralateral hippocampus to the affected side in patients with temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) and its relation with surgical outcome.

METHOD

We included 33 consecutive patients evaluated in our epilepsy surgery program during a five-year period. They underwent a presurgical MRI with volumetric T1 and diffusion weighted sequences. 22 patients with TLE-HS (13 women, 12 right TLE-HS) were finally selected. Median follow-up after surgery was 6.25 years (4.5-8.83 years). We segmented the hippocampal subfields of the contralateral hippocampus using FreeSurfer and calculated the fractional anisotropy (FA) and the mean diffusivity (MD) of each subfield. We also scanned 18 healthy age-matched controls.

RESULTS

After surgery, 50 % of the patients (n = 11) remained seizure-free (SF) following surgery. Comparing non-SF to SF patients, the MD showed increased values of the CA1 (p = 0.035), the molecular layer (p = 0.010) and the dentate gyrus (p = 0.041) in the healthy hippocampus. Using a cut-off point for a survival analysis, we found that patients with lower values of MD of the molecular layer and the CA1 remained SF during long-term post-operative follow-up (p < 0.0001).

CONCLUSIONS

The contralateral hippocampal internal microstructure may have be implicated in post-surgery seizure freedom in patients with TLE-HS.

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy

OBJECTIVE

The distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.

METHODS

T1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.

RESULTS

The SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.

CONCLUSION

The SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance.

Differences in functional connectivity profiles as a predictor of response to anterior thalamic nucleus deep brain stimulation for epilepsy: a hypothesis for the mechanism of action and a potential biomarker for outcomes

OBJECTIVE Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is a promising therapy for refractory epilepsy. Unfortunately, the variability in outcomes from ANT DBS is not fully understood. In this pilot study, the authors assess potential differences in functional connectivity related to the volume of tissue activated (VTA) in ANT DBS responders and nonresponders as a means for better understanding the mechanism of action and potentially improving DBS targeting. METHODS This retrospective analysis consisted of 6 patients who underwent ANT DBS for refractory epilepsy. Patients were classified as responders (n = 3) if their seizure frequency decreased by at least 50%. The DBS electrodes were localized postoperatively and VTAs were computationally generated based on DBS programming settings. VTAs were used as seed points for resting-state functional MRI connectivity analysis performed using a control dataset. Differences in cortical connectivity to the VTA were assessed between the responder and nonresponder groups. RESULTS The ANT DBS responders showed greater positive connectivity with the default mode network compared to nonresponders, including the posterior cingulate cortex, medial prefrontal cortex, inferior parietal lobule, and precuneus. Interestingly, there was also a consistent anticorrelation with the hippocampus seen in responders that was not present in nonresponders. CONCLUSIONS Based on their pilot study, the authors observed that successful ANT DBS in patients with epilepsy produces increased connectivity in the default mode network, which the authors hypothesize increases the threshold for seizure propagation. Additionally, an inhibitory effect on the hippocampus mediated through increased hippocampal γ-aminobutyric acid (GABA) concentration may contribute to seizure suppression. Future studies are planned to confirm these findings.

Investigating imaging network markers of cognitive dysfunction and pharmacoresistance in newly diagnosed epilepsy: a protocol for an observational cohort study in the UK

INTRODUCTION

Epilepsy is one of the most common serious brain disorders, characterised by seizures that severely affect a person's quality of life and, frequently, their cognitive and mental health. Although most existing work has examined chronic epilepsy, newly diagnosed patients present a unique opportunity to understand the underlying biology of epilepsy and predict effective treatment pathways. The objective of this prospective cohort study is to examine whether cognitive dysfunction is associated with measurable brain architectural and connectivity impairments at diagnosis and whether the outcome of antiepileptic drug treatment can be predicted using these measures.

METHODS AND ANALYSIS

107 patients with newly diagnosed focal epilepsy from two National Health Service Trusts and 48 healthy controls (aged 16-65 years) will be recruited over a period of 30 months. Baseline assessments will include neuropsychological evaluation, structural and functional Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), and a blood and saliva sample. Patients will be followed up every 6 months for a 24-month period to assess treatment outcomes. Connectivity- and network-based analyses of EEG and MRI data will be carried out and examined in relation to neuropsychological evaluation and patient treatment outcomes. Patient outcomes will also be investigated with respect to analysis of molecular isoforms of high mobility group box-1 from blood and saliva samples.

ETHICS AND DISSEMINATION

This study was approved by the North West, Liverpool East Research Ethics Committee (19/NW/0384) through the Integrated Research Application System (Project ID 260623). Health Research Authority (HRA) approval was provided on 22 August 2019. The project is sponsored by the UoL (UoL001449) and funded by a UK Medical Research Council (MRC) research grant (MR/S00355X/1). Findings will be presented at national and international meetings and conferences and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

IRAS Project ID 260623.

Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies

Drug-refractory focal epilepsies are network diseases associated with functional connectivity alterations both during ictal and interictal periods. A large majority of studies on the interictal/resting state have focused on functional MRI-based functional connectivity. Few studies have used electrophysiology, despite its high temporal capacities. In particular, stereotactic-EEG is highly suitable to study functional connectivity because it permits direct intracranial electrophysiological recordings with relative large-scale sampling. Most previous studies in stereotactic-EEG have been directed towards temporal lobe epilepsy, which does not represent the whole spectrum of drug-refractory epilepsies. The present study aims at filling this gap, investigating interictal functional connectivity alterations behind cortical epileptic organization and its association with post-surgical prognosis. To this purpose, we studied a large cohort of 59 patients with malformation of cortical development explored by stereotactic-EEG with a wide spatial sampling (76 distinct brain areas were recorded, median of 13.2 per patient). We computed functional connectivity using non-linear correlation. We focused on three zones defined by stereotactic-EEG ictal activity: the epileptogenic zone, the propagation zone and the non-involved zone. First, we compared within-zone and between-zones functional connectivity. Second, we analysed the directionality of functional connectivity between these zones. Third, we measured the associations between functional connectivity measures and clinical variables, especially post-surgical prognosis. Our study confirms that functional connectivity differs according to the zone under investigation. We found: (i) a gradual decrease of the within-zone functional connectivity with higher values for epileptogenic zone and propagation zone, and lower for non-involved zones; (ii) preferential coupling between structures of the epileptogenic zone; (iii) preferential coupling between epileptogenic zone and propagation zone; and (iv) poorer post-surgical outcome in patients with higher functional connectivity of non-involved zone (within- non-involved zone, between non-involved zone and propagation zone functional connectivity). Our work suggests that, even during the interictal state, functional connectivity is reinforced within epileptic cortices (epileptogenic zone and propagation zone) with a gradual organization. Moreover, larger functional connectivity alterations, suggesting more diffuse disease, are associated with poorer post-surgical prognosis. This is consistent with computational studies suggesting that connectivity is crucial in order to model the spatiotemporal dynamics of seizures.10.1093/brain/awy214_video1awy214media15833456182001.

Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning

Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming. We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging. A connectome-based prediction framework was developed using network properties from anatomically defined brain regions across the entire brain, which were used in a multi-task machine learning algorithm followed by support vector regression. Nodal eigenvector centrality, a measure of regional network integration, predicted approximately 60% of the variance in naming. The nodes with the highest regression weight were bilaterally distributed among perilimbic sub-networks involving mainly the medial and lateral temporal lobe regions. In the context of emerging evidence regarding the role of large structural networks that support language processing, our results suggest intact naming relies on the integration of sub-networks, as opposed to being dependent on isolated brain areas. In the case of TLE, these sub-networks may be disproportionately indicative naming processes that are dependent semantic integration from memory and lexical retrieval, as opposed to multi-modal perception or motor speech production.

Temporal lobe epilepsy: Hippocampal pathology modulates connectome topology and controllability

OBJECTIVE

To assess whether hippocampal sclerosis (HS) severity is mirrored at the level of large-scale networks.

METHODS

We studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 temporal lobe epilepsy (TLE) patients with histopathologic diagnosis of HS (n = 25; TLE-HS) and isolated gliosis (n = 19; TLE-G) and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm that simulates functional dynamics from structural network data.

RESULTS

Compared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from gray matter atrophy.

CONCLUSIONS

Severe HS is associated with marked remodeling of connectome topology and structurally governed functional dynamics in TLE, as opposed to isolated gliosis, which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales.

Network based statistics reveals trophic and neuroprotective effect of early high dose erythropoetin on brain connectivity in very preterm infants

Periventricular white matter injury is common in very preterm infants and it is associated with long term neurodevelopmental impairments. While evidence supports the protective effects of erythropoetin (EPO) in preventing injury, we currently lack the complete understanding of how EPO affects the emergence and maturation of anatomical brain connectivity and function. In this case-control study, connectomic analysis based on diffusion MRI tractography was applied to evaluate the effect of early high-dose EPO in preterm infants. A whole brain, network-level analysis revealed a sub-network of anatomical brain connections in which connectivity strengths were significantly stronger in the EPO group. This distributed network comprised connections predominantly in the frontal and temporal lobe bilaterally, and the effect of EPO was focused on peripheral and feeder connections of the core structural connectivity network. EPO resulted in a globally increased clustering coefficient, higher global and average local efficiency, while higher strength and increased clustering was found for regions in the frontal lobe and cingulate gyrus. The connectivity network most affected by the EPO treatment showed a steeper increase graph theoretical measures with age compared to the placebo group. Our results demonstrate a weak but widespread effect of EPO on the structural connectivity network and a possible trophic effect of EPO reflected by increasing network segregation, predominantly in local connections.

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Erythropoietin (EPO) is a potential neuroprotective agent in very preterm infants.

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EPO leads to increased structural brain connectivity in fronto-temporal regions.

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Clustering coefficient, local and global efficiency increases after EPO treatment.

Resting-state functional brain networks in adults with a new diagnosis of focal epilepsy

OBJECTIVES

Newly diagnosed focal epilepsy (NDfE) is rarely studied, particularly using advanced neuroimaging techniques. Many patients with NDfE experience cognitive impairments, particularly with respect to memory, sustained attention, mental flexibility, and executive functioning. Cognitive impairments have been related to alterations in resting-state functional brain networks in patients with neurological disorders. In the present study, we investigated whether patients with NDfE had altered connectivity in large-scale functional networks using resting-state functional MRI.

METHODS

We recruited 27 adults with NDfE and 36 age- and sex-matched healthy controls. Resting-state functional MRI was analyzed using the Functional Connectivity Toolbox (CONN). We investigate reproducibly determined large-scale functional networks, including the default mode, salience, fronto-parietal attention, sensorimotor, and language networks using a seed-based approach. Network comparisons between patients and controls were thresholded using a FDR cluster-level correction approach.

RESULTS

We found no significant differences in functional connectivity between seeds within the default mode, salience, sensorimotor, and language networks and other regions of the brain between patients and controls. However, patients with NDfE had significantly reduced connectivity between intraparietal seeds within the fronto-parietal attention network and predominantly frontal and temporal cortical regions relative to controls; this finding was demonstrated including and excluding the patients with brain lesions. No common alteration in brain structure was observed in patients using voxel-based morphometry. Findings were not influenced by treatment outcome at 1 year.

CONCLUSIONS

Patients with focal epilepsy have brain functional connectivity alterations at diagnosis. Functional brain abnormalities are not necessarily a consequence of the chronicity of epilepsy and are present when seizures first emerge.

Resting state connectivity in neocortical epilepsy: The epilepsy network as a patient-specific biomarker

OBJECTIVE

Localization related epilepsy (LRE) is increasingly accepted as a network disorder. To better understand the network specific characteristics of LRE, we defined individual epilepsy networks and compared them across patients.

METHODS

The epilepsy network was defined in the slow cortical potential frequency band in 10 patients using intracranial EEG data obtained during interictal periods. Cortical regions were included in the epilepsy network if their connectivity pattern was similar to the connectivity pattern of the seizure onset electrode contact. Patients were subdivided into frontal, temporal, and posterior quadrant cohorts according to the anatomic location of seizure onset. Jaccard similarity was calculated within each cohort to assess for similarity of the epilepsy network between patients within each cohort.

RESULTS

All patients exhibited an epilepsy network in the slow cortical potential frequency band. The topographic distribution of this correlated network activity was found to be unique at the single subject level.

CONCLUSIONS

The epilepsy network was unique at the single patient level, even between patients with similar seizure onset locations.

SIGNIFICANCE

We demonstrated that the epilepsy network is patient-specific. This is in keeping with our current understanding of brain networks and identifies the patient-specific epilepsy network as a possible biomarker in LRE.

Advanced lesion symptom mapping analyses and implementation as BCBtoolkit

Background

Patients with brain lesions provide a unique opportunity to understand the functioning of the human mind. However, even when focal, brain lesions have local and remote effects that impact functionally and structurally connected circuits. Similarly, function emerges from the interaction between brain areas rather than their sole activity. For instance, category fluency requires the associations between executive, semantic, and language production functions.

Findings

Here, we provide, for the first time, a set of complementary solutions for measuring the impact of a given lesion on the neuronal circuits. Our methods, which were applied to 37 patients with a focal frontal brain lesions, revealed a large set of directly and indirectly disconnected brain regions that had significantly impacted category fluency performance. The directly disconnected regions corresponded to areas that are classically considered as functionally engaged in verbal fluency and categorization tasks. These regions were also organized into larger directly and indirectly disconnected functional networks, including the left ventral fronto-parietal network, whose cortical thickness correlated with performance on category fluency.

Conclusions

The combination of structural and functional connectivity together with cortical thickness estimates reveal the remote effects of brain lesions, provide for the identification of the affected networks, and strengthen our understanding of their relationship with cognitive and behavioral measures. The methods presented are available and freely accessible in the BCBtoolkit as supplementary software [1].

Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy

There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.

Microstructure-Informed Connectomics: Enriching Large-Scale Descriptions of Healthy and Diseased Brains

Rapid advances in neuroimaging and network science have produced powerful tools and measures to appreciate human brain organization at multiple spatial and temporal scales. It is now possible to obtain increasingly meaningful representations of whole-brain structural and functional brain networks and to formally assess macroscale principles of network topology. In addition to its utility in characterizing healthy brain organization, individual variability, and life span-related changes, there is high promise of network neuroscience for the conceptualization and, ultimately, management of brain disorders. In the current review, we argue for a science of the human brain that, while strongly embracing macroscale connectomics, also recommends awareness of brain properties derived from meso- and microscale resolutions. Such features include MRI markers of tissue microstructure, local functional properties, as well as information from nonimaging domains, including cellular, genetic, or chemical data. Integrating these measures with connectome models promises to better define the individual elements that constitute large-scale networks, and clarify the notion of connection strength among them. By enriching the description of large-scale networks, this approach may improve our understanding of fundamental principles of healthy brain organization. Notably, it may also better define the substrate of prevalent brain disorders, including stroke, autism, as well as drug-resistant epilepsies that are each characterized by intriguing interactions between local anomalies and network-level perturbations.

Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery

OBJECTIVE

We evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference from clinical variables in patients with mesial temporal lobe epilepsy (TLE).

METHODS

Fifty patients with unilateral TLE were classified either as having persistent disabling seizures (SZ) or becoming seizure-free (SZF) at least 1 year after epilepsy surgery. Their presurgical structural connectomes were reconstructed from whole-brain diffusion tensor imaging. A deep network was trained based on connectome data to classify seizure outcome using 5-fold cross-validation.

RESULTS

Classification accuracy of our trained neural network showed positive predictive value (PPV; seizure freedom) of 88 ± 7% and mean negative predictive value (NPV; seizure refractoriness) of 79 ± 8%. Conversely, a classification model based on clinical variables alone yielded <50% accuracy. The specific features that contributed to high accuracy classification of the neural network were located not only in the ipsilateral temporal and extratemporal regions, but also in the contralateral hemisphere.

SIGNIFICANCE

Deep learning demonstrated to be a powerful statistical approach capable of isolating abnormal individualized patterns from complex datasets to provide a highly accurate prediction of seizure outcomes after surgery. Features involved in this predictive model were both ipsilateral and contralateral to the clinical foci and spanned across limbic and extralimbic networks.

White matter network topology relates to cognitive flexibility and cumulative neurological risk in adult survivors of pediatric brain tumors

Adult survivors of pediatric brain tumors exhibit deficits in executive functioning. Given that brain tumors and medical treatments for brain tumors result in disruptions to white matter, a network analysis was used to explore the topological properties of white matter networks. This study used diffusion tensor imaging and deterministic tractography in 38 adult survivors of pediatric brain tumors (mean age in years = 23.11 (SD = 4.96), 54% female, mean years post diagnosis = 14.09 (SD = 6.19)) and 38 healthy peers matched by age, gender, handedness, and socioeconomic status. Nodes were defined using the Automated Anatomical Labeling (AAL) parcellation scheme, and edges were defined as the mean fractional anisotropy of streamlines that connected each node pair. Global efficiency and average clustering coefficient were reduced in survivors compared to healthy peers with preferential impact to hub regions. Global efficiency mediated differences in cognitive flexibility between survivors and healthy peers, as well as the relationship between cumulative neurological risk and cognitive flexibility. These results suggest that adult survivors of pediatric brain tumors, on average one and a half decades post brain tumor diagnosis and treatment, exhibit altered white matter topology in the form of suboptimal integration and segregation of large scale networks, and that disrupted topology may underlie executive functioning impairments. Network based studies provided important topographic insights on network organization in long-term survivors of pediatric brain tumor.

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Long term brain tumor survivorship is associated with altered white matter networks.

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Hub regions were preferentially impacted in survivors.

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Network properties explain cognitive flexibility differences between survivors and peers.

Seizure outcomes of supratentorial brain tumor resection in pediatric patients

Background

This study aims to identify the prevalence of and risk factors for seizure development after supratentorial brain tumor resection in pediatric patients. This could be used to guide the postoperative management and usage of anti-epileptic drugs (AEDs).

Methods

Retrospective study was conducted for patients between 0 and 21 years with supratentorial tumor resection between 2005 and 2015 at a single institution.

Results

Two hundred patients (114 males/86 females) were identified. Median age at resection (±SD) was 9.025 ± 5.720 years and mean follow-up was 4 ± 2 years. Resection was gross total in 82 patients (41%) and partial in 118 patients (59%); 66 patients (33%) experienced preoperative seizures, and 67 patients (34%) experienced postoperative seizures; 18 patients (27%) had early seizures, and 49 patients (73%) had late seizures. Univariate analysis identified risk factors for postoperative seizures as: preoperative seizures (P < 0.001), age less than 2 years (P = 0.003), temporal location (P < 0.001), thalamic location (P = 0.017), preoperative hyponatremia (P = 0.017), World Health Organization grade (P = 0.008), and pathology (P = 0.005). Multivariate regression identified 5 robust risk factors: temporal location (odds ratio [OR] 4.7, 95% CI: 1.7-13.3, P = 0.003), age <2 years (OR 3.9, 95% CI: 1.0-15.4; P = 0.049), preoperative hydrocephalus (OR 3.8, 95% CI: 1.5-9.4; P = 0.005), preoperative seizure (OR 2.8, 95% CI: 1.2-6.5; P = 0.016) and parietal location (OR 0.25, 95% CI: 0.06-0.99; P = 0.049). Extent of resection did not correlate with seizure development (P > 0.05).

Conclusions

This study reveals 5 risk factors for postoperative seizures after resection of supratentorial tumors. These factors should be considered in postoperative management of these patients.

Alterations of network synchrony after epileptic seizures: An analysis of post-ictal intracranial recordings in pediatric epilepsy patients

OBJECTIVE

Post-ictal EEG alterations have been identified in studies of intracranial recordings, but the clinical significance of post-ictal EEG activity is undetermined. The purpose of this study was to examine the relationship between peri-ictal EEG activity, surgical outcome, and extent of seizure propagation in a sample of pediatric epilepsy patients.

METHODS

Intracranial EEG recordings were obtained from 19 patients (mean age = 11.4 years, range = 3-20 years) with 57 seizures used for analysis (mean = 3.0 seizures per patient). For each seizure, 3-min segments were extracted from adjacent pre-ictal and post-ictal epochs. To compare physiology of the epileptic network between epochs, we calculated the relative delta power (Δ) using discrete Fourier transformation and constructed functional networks based on broadband connectivity (conn). We investigated differences between the pre-ictal (Δ_pre, conn_pre) and post-ictal (Δ_post, conn_post) segments in focal-network (i.e., confined to seizure onset zone) versus distributed-network (i.e., diffuse ictal propagation) seizures.

RESULTS

Distributed-network (DN) seizures exhibited increased post-ictal delta power and global EEG connectivity compared to focal-network (FN) seizures. Following DN seizures, patients with seizure-free outcomes exhibited a 14.7% mean increase in delta power and an 8.3% mean increase in global connectivity compared to pre-ictal baseline, which was dramatically less than values observed among seizure-persistent patients (29.6% and 47.1%, respectively).

SIGNIFICANCE

Post-ictal differences between DN and FN seizures correlate with post-operative seizure persistence. We hypothesize that post-ictal deactivation of subcortical nuclei recruited during seizure propagation may account for this result while lending insights into mechanisms of post-operative seizure recurrence.

Probing the reproducibility of quantitative estimates of structural connectivity derived from global tractography

As quantitative measures derived from fiber tractography are increasingly being used to characterize the structural connectivity of the brain, it is important to establish their reproducibility. However, no such information is as yet available for global tractography. Here we provide the first comprehensive analysis of the reproducibility of streamline counts derived from global tractography as quantitative estimates of structural connectivity. In a sample of healthy young adults scanned twice within one week, within-session and between-session test-retest reproducibility was estimated for streamline counts of connections based on regions of the AAL atlas using the intraclass correlation coefficient (ICC) for absolute agreement. We further evaluated the influence of the type of head-coil (12 versus 32 channels) and the number of reconstruction repetitions (reconstructing streamlines once or aggregated over ten repetitions). Factorial analyses demonstrated that reproducibility was significantly greater for within- than between-session reproducibility and significantly increased by aggregating streamline counts over ten reconstruction repetitions. Using a high-resolution head-coil incurred only small beneficial effects. Overall, ICC values were positively correlated with the streamline count of a connection. Additional analyses assessed the influence of different selection variants (defining fuzzy versus no fuzzy borders of the seed mask; selecting streamlines that end in versus pass through a seed) showing that an endpoint-based variant using fuzzy selection provides the best compromise between reproducibility and anatomical specificity. In sum, aggregating quantitative indices over repeated estimations and higher numbers of streamlines are important determinants of test-retest reproducibility. If these factors are taken into account, streamline counts derived from global tractography provide an adequately reproducible quantitative measure that can be used to gauge the structural connectivity of the brain in health and disease.

Facial emotion processing in patients with seizure disorders

Studies of emotion processing are needed to better understand the pathophysiology of psychogenic nonepileptic seizures (PNES). We examined the differences in facial emotion processing between 12 patients with PNES, 12 patients with temporal lobe epilepsy (TLE), and 24 matched healthy controls (HCs) using fMRI with emotional faces task (EFT) (happy/sad/fearful/neutral) and resting state connectivity. Compared with TLE, patients with PNES exhibited increased fMRI response to happy, neutral, and fearful faces in visual, temporal, and/or parietal regions and decreased fMRI response to sad faces in the putamen bilaterally. Regions showing significant differences between PNES and TLE were used as functional seed regions of interest (ROIs), in addition to amygdala structural seed ROIs for resting state functional connectivity analyses. Whole brain analyses showed that compared with TLE and HCs, patients with PNES exhibited increased functional connectivity of the functional seed ROIs to several brain regions, particularly to cerebellar, visual, motor, and frontotemporal regions. Connectograms showed increased functional connections between left parahippocampal gyrus/uncus ROIs and right temporal ROIs in PNES compared with both the TLE and HC groups. Resting state functional connectivity of the left and right amygdala to various brain regions including emotion regulation and motor control circuits was increased in PNES when compared with those with TLE. This study provides preliminary evidence that patients with PNES exhibit altered facial emotion processing compared with patients with TLE and HCs and increased amygdala functional connectivity compared with TLE. These findings identify potential key differences in facial emotion processing reflective of neurophysiologic markers of neural circuitry alterations that can be used to generate further hypotheses for developing studies that examine the contributions of emotion processing to the development and maintenance of PNES.

The impact of epilepsy surgery on the structural connectome and its relation to outcome

Background

Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome.

Methods

We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks.

Results

Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole.

Conclusion

Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.

Histological and MRI markers of white matter damage in focal epilepsy

Growing evidence highlights the importance of white matter in the pathogenesis of focal epilepsy. Ex vivo and post-mortem studies show pathological changes in epileptic patients in white matter myelination, axonal integrity, and cellular composition. Diffusion-weighted MRI and its analytical extensions, particularly diffusion tensor imaging (DTI), have been the most widely used technique to image the white matter in vivo for the last two decades, and have shown microstructural alterations in multiple tracts both in the vicinity and at distance from the epileptogenic focus. These techniques have also shown promising ability to predict cognitive status and response to pharmacological or surgical treatments. More recently, the hypothesis that focal epilepsy may be more adequately described as a system-level disorder has motivated a shift towards the study of macroscale brain connectivity. This review will cover emerging findings contributing to our understanding of white matter alterations in focal epilepsy, studied by means of histological and ultrastructural analyses, diffusion MRI, and large-scale network analysis. Focus is put on temporal lobe epilepsy and focal cortical dysplasia. This topic was addressed in a special interest group on neuroimaging at the 70th annual meeting of the American Epilepsy Society, held in Houston December 2-6, 2016.

Neuroimaging in Epilepsy

In recent years, the field of neuroimaging has undergone dramatic development. Specifically, of importance for clinicians and researchers managing patients with epilepsies, new methods of brain imaging in search of the seizure-producing abnormalities have been implemented, and older methods have undergone additional refinement. Methodology to predict seizure freedom and cognitive outcome has also rapidly progressed. In general, the image data processing methods are very different and more complicated than even a decade ago. In this review, we identify the recent developments in neuroimaging that are aimed at improved management of epilepsy patients. Advances in structural imaging, diffusion imaging, fMRI, structural and functional connectivity, hybrid imaging methods, quantitative neuroimaging, and machine-learning are discussed. We also briefly summarize the potential new developments that may shape the field of neuroimaging in the near future and may advance not only our understanding of epileptic networks as the source of treatment-resistant seizures but also better define the areas that need to be treated in order to provide the patients with better long-term outcomes.

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

OBJECTIVE

To characterize the presurgical brain functional architecture presented in patients with temporal lobe epilepsy (TLE) using graph theoretical measures of resting-state fMRI data and to test its association with surgical outcome.

METHODS

Fifty-six unilateral patients with TLE, who subsequently underwent anterior temporal lobectomy and were classified as obtaining a seizure-free (Engel class I, n = 35) vs not seizure-free (Engel classes II-IV, n = 21) outcome at 1 year after surgery, and 28 matched healthy controls were enrolled. On the basis of their presurgical resting-state functional connectivity, network properties, including nodal hubness (importance of a node to the network; degree, betweenness, and eigenvector centralities) and integration (global efficiency), were estimated and compared across our experimental groups. Cross-validations with support vector machine (SVM) were used to examine whether selective nodal hubness exceeded standard clinical characteristics in outcome prediction.

RESULTS

Compared to the seizure-free patients and healthy controls, the not seizure-free patients displayed a specific increase in nodal hubness (degree and eigenvector centralities) involving both the ipsilateral and contralateral thalami, contributed by an increase in the number of connections to regions distributed mostly in the contralateral hemisphere. Simulating removal of thalamus reduced network integration more dramatically in not seizure-free patients. Lastly, SVM models built on these thalamic hubness measures produced 76% prediction accuracy, while models built with standard clinical variables yielded only 58% accuracy (both were cross-validated).

CONCLUSIONS

A thalamic network associated with seizure recurrence may already be established presurgically. Thalamic hubness can serve as a potential biomarker of surgical outcome, outperforming the clinical characteristics commonly used in epilepsy surgery centers.

Magnetic resonance imaging connectivity for the prediction of seizure outcome in temporal lobe epilepsy

OBJECTIVE

Currently, approximately 60-70% of patients with unilateral temporal lobe epilepsy (TLE) remain seizure-free 3 years after surgery. The goal of this work was to develop a presurgical connectivity-based biomarker to identify those patients who will have an unfavorable seizure outcome 1-year postsurgery.

METHODS

Resting-state functional and diffusion-weighted 3T magnetic resonance imaging (MRI) was acquired from 22 unilateral (15 right, 7 left) patients with TLE and 35 healthy controls. A seizure propagation network was identified including ipsilateral (to seizure focus) and contralateral hippocampus, thalamus, and insula, with bilateral midcingulate and precuneus. Between each pair of regions, functional connectivity based on correlations of low frequency functional MRI signals, and structural connectivity based on streamline density of diffusion MRI data were computed and transformed to metrics related to healthy controls of the same age.

RESULTS

A consistent connectivity pattern representing the network expected in patients with seizure-free outcome was identified using eight patients who were seizure-free at 1-year postsurgery. The hypothesis that increased similarity to the model would be associated with better seizure outcome was tested in 14 other patients (Engel class IA, seizure-free: n = 5; Engel class IB-II, favorable: n = 4; Engel class III-IV, unfavorable: n = 5) using two similarity metrics: Pearson correlation and Euclidean distance. The seizure-free connectivity model successfully separated all the patients with unfavorable outcome from the seizure-free and favorable outcome patients (p = 0.0005, two-tailed Fisher's exact test) through the combination of the two similarity metrics with 100% accuracy. No other clinical and demographic predictors were successful in this regard.

SIGNIFICANCE

This work introduces a methodologic framework to assess individual patients, and demonstrates the ability to use network connectivity as a potential clinical tool for epilepsy surgery outcome prediction after more comprehensive validation.

Elucidation of White Matter Tracts of the Human Amygdala by Detailed Comparison between High-Resolution Postmortem Magnetic Resonance Imaging and Histology

The amygdala has attracted considerable research interest because of its potential involvement in various neuropsychiatric disorders. Recently, attempts have been made using magnetic resonance imaging (MRI) to evaluate the integrity of the axonal connections to and from the amygdala under pathological conditions. Although amygdalar pathways have been studied extensively in animal models, anatomical references for the human brain are limited to histology-based resources from a small number of slice locations, orientations and annotations. In the present study, we performed high-resolution (250 μm) MRI of postmortem human brains followed by serial histology sectioning. The histology data were used to identify amygdalar pathways, and the anatomical delineation of the assigned structures was extended into 3D using the MRI data. We were able to define the detailed anatomy of the stria terminalis and amygdalofugal pathway, as well as the anatomy of the nearby basal forebrain areas, including the substantia innominata. The present results will help us understand in detail the white matter structures associated with the amygdala, and will serve as an anatomical reference for the design of in vivo MRI studies and interpretation of their data.

Blood DNA methylation pattern is altered in mesial temporal lobe epilepsy

Mesial temporal lobe epilepsy (MTLE) is a common epileptic disorder; little is known whether it is associated with peripheral epigenetic changes. Here we compared blood whole genomic DNA methylation pattern in MTLE patients (n = 30) relative to controls (n = 30) with the Human Methylation 450 K BeadChip assay, and explored genes and pathways that were differentially methylated using bioinformatics profiling. The MTLE and control groups showed significantly different (P < 1.03e-07) DNA methylation at 216 sites, with 164 sites involved hyper- and 52 sites hypo- methylation. Two hyper- and 32 hypo-methylated sites were associated with promoters, while 87 hyper- and 43 hypo-methylated sites corresponded to coding regions. The differentially methylated genes were largely related to pathways predicted to participate in anion binding, oxidoreductant activity, growth regulation, skeletal development and drug metabolism, with the most distinct ones included SLC34A2, CLCN6, CLCA4, CYP3A43, CYP3A4 and CYP2C9. Among the MTLE patients, panels of genes also appeared to be differentially methylated relative to disease duration, resistance to anti-epileptics and MRI alterations of hippocampal sclerosis. The peripheral epigenetic changes observed in MTLE could be involved in certain disease-related modulations and warrant further translational investigations.

18F-FDG-PET patterns of surgical success and failure in mesial temporal lobe epilepsy

OBJECTIVE

To search for [¹⁸F]-fluorodeoxyglucose (FDG)-PET patterns predictive of long-term prognosis in surgery for drug-resistant mesial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis (HS).

METHODS

We analyzed metabolic data with [¹⁸F]-FDG-PET in 97 patients with MTLE (53 female participants; age range 15-56 years) with unilateral HS (50 left) and compared the metabolic patterns, electroclinical features, and structural atrophy on MRI in patients with the best outcome after anteromesial temporal resection (Engel class IA, completely seizure-free) to those with a non-IA outcome, including suboptimal outcome and failure. Imaging processing was performed with statistical parametric mapping (SPM5).

RESULTS

With a mean follow-up of >6 years (range 2-14 years), 85% of patients achieved a class I outcome, including 45% in class IA. Class IA outcome was associated with a focal anteromesial temporal hypometabolism, whereas non-IA outcome correlated with extratemporal metabolic changes that differed according to the lateralization: ipsilateral mesial frontal and perisylvian hypometabolism in right HS and contralateral fronto-insular hypometabolism and posterior white matter hypermetabolism in left HS. Suboptimal outcome presented a metabolic pattern similar to the best outcome but with a larger involvement of extratemporal areas, including the contralateral side in left HS. Failure was characterized by a mild temporal involvement sparing the hippocampus and relatively high extratemporal hypometabolism on both sides. These findings were concordant with electroclinical features reflecting the organization of the epileptogenic zone but were independent of the structural abnormalities detected on MRI.

CONCLUSIONS

[¹⁸F]-FDG-PET patterns help refine the prognostic factors in MTLE and should be implemented in predictive models for epilepsy surgery.

Preoperative automated fibre quantification predicts postoperative seizure outcome in temporal lobe epilepsy

Approximately one in every two patients with pharmacoresistant temporal lobe epilepsy will not be rendered completely seizure-free after temporal lobe surgery. The reasons for this are unknown and are likely to be multifactorial. Quantitative volumetric magnetic resonance imaging techniques have provided limited insight into the causes of persistent postoperative seizures in patients with temporal lobe epilepsy. The relationship between postoperative outcome and preoperative pathology of white matter tracts, which constitute crucial components of epileptogenic networks, is unknown. We investigated regional tissue characteristics of preoperative temporal lobe white matter tracts known to be important in the generation and propagation of temporal lobe seizures in temporal lobe epilepsy, using diffusion tensor imaging and automated fibre quantification. We studied 43 patients with mesial temporal lobe epilepsy associated with hippocampal sclerosis and 44 healthy controls. Patients underwent preoperative imaging, amygdalohippocampectomy and postoperative assessment using the International League Against Epilepsy seizure outcome scale. From preoperative imaging, the fimbria-fornix, parahippocampal white matter bundle and uncinate fasciculus were reconstructed, and scalar diffusion metrics were calculated along the length of each tract. Altogether, 51.2% of patients were rendered completely seizure-free and 48.8% continued to experience postoperative seizure symptoms. Relative to controls, both patient groups exhibited strong and significant diffusion abnormalities along the length of the uncinate bilaterally, the ipsilateral parahippocampal white matter bundle, and the ipsilateral fimbria-fornix in regions located within the medial temporal lobe. However, only patients with persistent postoperative seizures showed evidence of significant pathology of tract sections located in the ipsilateral dorsal fornix and in the contralateral parahippocampal white matter bundle. Using receiver operating characteristic curves, diffusion characteristics of these regions could classify individual patients according to outcome with 84% sensitivity and 89% specificity. Pathological changes in the dorsal fornix were beyond the margins of resection, and contralateral parahippocampal changes may suggest a bitemporal disorder in some patients. Furthermore, diffusion characteristics of the ipsilateral uncinate could classify patients from controls with a sensitivity of 98%; importantly, by co-registering the preoperative fibre maps to postoperative surgical lacuna maps, we observed that the extent of uncinate resection was significantly greater in patients who were rendered seizure-free, suggesting that a smaller resection of the uncinate may represent insufficient disconnection of an anterior temporal epileptogenic network. These results may have the potential to be developed into imaging prognostic markers of postoperative outcome and provide new insights for why some patients with temporal lobe epilepsy continue to experience postoperative seizures.

Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling

See Eissa and Schevon (doi:10.1093/aww332) for a scientific commentary on this article.

Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.