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

Individual cerebellar metabolic connectome in patients with MTLE and NTLE associated with surgical prognosis

PURPOSE

This study aimed to comprehensively explore the different metabolic connectivity topological changes in MTLE and NTLE, as well as their association with surgical outcomes.

METHODS

This study enrolled a cohort of patients with intractable MTLE and NTLE. Each individual's metabolic connectome, as determined by Kullback-Leibler divergence similarity estimation for the [18F]FDG PET image, was employed to conduct a comprehensive analysis of the cerebral metabolic network. Alterations in network connectivity were assessed by extracting and evaluating the strength of edge and weighted connectivity. By utilizing these two connectivity strength metrics with the cerebellum, we explored the network properties of connectivity and its association with prognosis in surgical patients.

RESULTS

Both MTLE and NTLE patients exhibited substantial alterations in the connectivity of the metabolic network at the edge and nodal levels (p < 0.01, FDR corrected). The key disparity between MTLE and NTLE was observed in the cerebellum. In MTLE, there was a predominance of increased connectivity strength in the cerebellum. Whereas, a decrease in cerebellar connectivity was identified in NTLE. It was found that in MTLE, higher edge connectivity and weighted connectivity strength in the contralateral cerebellar hemisphere correlated with improved surgical outcomes. Conversely, in NTLE, a higher edge metabolic connectivity strength in the ipsilateral cerebellar hemisphere suggested a worse surgical prognosis.

CONCLUSION

The cerebellum exhibits distinct topological characteristics in the metabolic networks between MTLE and NTLE. The hyper- or hypo-metabolic connectivity in the cerebellum may be a prognostic biomarker of surgical prognosis, which might aid in therapeutic decision-making for TLE individuals.

Leveraging the resting brain to predict memory decline after temporal lobectomy

OBJECTIVE

Predicting memory morbidity after temporal lobectomy in patients with temporal lobe epilepsy (TLE) relies on indices of preoperative temporal lobe structural and functional integrity. However, epilepsy is increasingly considered a network disorder, and memory a network phenomenon. We assessed the utility of functional network measures to predict postoperative memory changes.

METHODS

Seventy-two adults with TLE (37 left/35 right) underwent preoperative resting-state functional magnetic resonance imaging and pre- and postoperative neuropsychological assessment. We compared functional connectivity throughout the memory network of each patient to a healthy control template (n = 19) to identify differences in global organization. A second metric indicated the degree of integration of the to-be-resected temporal lobe with the rest of the memory network. We included these measures in a linear regression model alongside standard clinical variables as predictors of memory change after surgery.

RESULTS

Left TLE patients with more atypical memory networks, and with greater functional integration of the to-be-resected region with the rest of the memory network preoperatively, experienced the greatest decline in verbal memory after surgery. Together, these two measures explained 44% of variance in verbal memory change, outperforming standard clinical and demographic variables. None of the variables examined was associated with visuospatial memory change in patients with right TLE.

SIGNIFICANCE

Resting-state connectivity provides valuable information concerning both the integrity of to-be-resected tissue and functional reserve across memory-relevant regions outside of the to-be-resected tissue. Intrinsic functional connectivity has the potential to be useful for clinical decision-making regarding memory outcomes in left TLE, and more work is needed to identify the factors responsible for differences seen in right TLE.

Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

BACKGROUND AND OBJECTIVES

Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain's structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship affects the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control after surgery.

METHODS

We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using presurgery functional data from intracranial EEG (iEEG) recordings, presurgery and postsurgery structural data from T1-weighted MRI, and presurgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using the Spearman rank correlation and analyzed this structure-function coupling at 2 spatial scales: (1) global iEEG network level and (2) individual iEEG electrode contacts using virtual surgeries. We retrospectively predicted postoperative seizure freedom by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model.

RESULTS

We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free patients compared with those with seizure recurrence (p = 0.002, d = 0.76, area under the receiver operating characteristic curve [AUC] = 0.78 [95% CI 0.62-0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p = 0.007, d = 0.96, AUC = 0.73 [95% CI 0.58-0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 (95% CI 0.67-0.94). These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 (95% CI 0.82-1.0), accuracy of 92%, sensitivity of 93%, and specificity of 91%.

DISCUSSION

Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into presurgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery.

CLASSIFICATION OF EVIDENCE

This is a Class IV retrospective case series showing that structure-function mapping may help determine the outcome from surgical resection for treatment-resistant focal epilepsy.

Neurocysticercosis-related Seizures: Imaging Biomarkers

Neurocysticercosis (NCC)-a parasitic CNS infection endemic to developing nations-has been called the leading global cause of acquired epilepsy yet remains understudied. It is currently unknown why a large proportion of patients develop recurrent seizures, often following the presentation of acute seizures. Furthermore, the presentation of NCC is heterogenous and the features that predispose to the development of an epileptogenic state remain uncertain. Perilesional factors (such as oedema and gliosis) have been implicated in NCC-related ictogenesis, but the effects of cystic factors, including lesion load and location, seem not to play a role in the development of habitual epilepsy. In addition, the cytotoxic consequences of the cyst's degenerative stages are varied and the majority of research, relying on retrospective data, lacks the necessary specificity to distinguish between acute symptomatic and unprovoked seizures. Previous research has established that epileptogenesis can be the consequence of abnormal network connectivity, and some imaging studies have suggested that a causative link may exist between NCC and aberrant network organisation. In wider epilepsy research, network approaches have been widely adopted; studies benefiting predominantly from the rich, multimodal data provided by advanced MRI methods are at the forefront of the field. Quantitative MRI approaches have the potential to elucidate the lesser-understood epileptogenic mechanisms of NCC. This review will summarise the current understanding of the relationship between NCC and epilepsy, with a focus on MRI methodologies. In addition, network neuroscience approaches with putative value will be highlighted, drawing from current imaging trends in epilepsy research.

MicroRNAs as Biomarkers of Surgical Outcome in Mesial Temporal Lobe Epilepsy: A Systematic Review

Mesial temporal lobe epilepsy is the most common type of epilepsy. For most patients suffering from TLE, the only treatment option is surgery. However, there is a high possibility of relapse. Invasive EEG as a method for predicting the outcome of surgical treatment is a very complex and invasive manipulation, so the search for outcome biomarkers is an urgent task. MicroRNAs as potential biomarkers of surgical outcome are the subject of this study. For this study, a systematic search for publications in databases such as PubMed, Springer, Web of Science, Scopus, ScienceDirect, and MDPI was carried out. The following keywords were used: temporal lobe epilepsy, microRNA, biomarkers, surgery, and outcome. Three microRNAs were studied as prognostic biomarkers of surgical outcome: miR-27a-3p, miR-328-3p, and miR-654-3p. According to the results of the study, only miR-654-3p showed a good ability to discriminate between patients with poor and good surgical outcomes. MiR-654-3p is involved in the following biological pathways: ATP-binding cassette drug transporters, glutamate transporter SLC7A11, and TP53. A specific target for miR-654-3p is GLRA2, the glycine receptor subunit. MicroRNAs, which are diagnostic biomarkers of TLE, and epileptogenesis, miR-134-5p, MiR-30a, miRs-143, etc., can be considered as potential biomarkers of surgical outcome, as they can be indicators of early and late relapses. These microRNAs are involved in the processes characteristic of epilepsy: oxidative stress and apoptosis. The study of miRNAs as potential predictive biomarkers of surgical outcome is an urgent task and should be continued. However, when studying miRNA expression profiles, it is important to take into account and note a number of factors, such as the type of sample under study, the time of sampling for the study, the type and duration of the disease, and the type of antiepileptic treatment. Without taking into account all these factors, it is impossible to assess the influence and involvement of miRNAs in epileptic processes.

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.

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI

Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0.84 ± 0.08 and 0.74 ± 0.22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0.84-0.86) as reported in the literature. Median 95 % Hausdorff distances were 3.6 mm and 4.0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0.94, p < 0.0001) and held-out test subjects (r = 0.87, p < 0.0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0.5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0.90, p < 0.0001, mean absolute error = 6.3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.

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.

Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond

Self-organized neuronal oscillations rely on precisely orchestrated ensemble activity in reverberating neuronal networks. Chronic, non-malignant disorders of the brain are often coupled to pathological neuronal activity patterns. In addition to the characteristic behavioral symptoms, these disturbances are giving rise to both transient and persistent changes of various brain rhythms. Increasing evidence support the causal role of these "oscillopathies" in the phenotypic emergence of the disease symptoms, identifying neuronal network oscillations as potential therapeutic targets. While the kinetics of pharmacological therapy is not suitable to compensate the disease related fine-scale disturbances of network oscillations, external biophysical modalities (e.g., electrical stimulation) can alter spike timing in a temporally precise manner. These perturbations can warp rhythmic oscillatory patterns via resonance or entrainment. Properly timed phasic stimuli can even switch between the stable states of networks acting as multistable oscillators, substantially changing the emergent oscillatory patterns. Novel transcranial electric stimulation (TES) approaches offer more reliable neuronal control by allowing higher intensities with tolerable side-effect profiles. This precise temporal steerability combined with the non- or minimally invasive nature of these novel TES interventions make them promising therapeutic candidates for functional disorders of the brain. Here we review the key experimental findings and theoretical background concerning various pathological aspects of neuronal network activity leading to the generation of epileptic seizures. The conceptual and practical state of the art of temporally targeted brain stimulation is discussed focusing on the prevention and early termination of epileptic seizures.

Disrupted functional connectivity in white matter resting-state networks in unilateral temporal lobe epilepsy

Unilateral temporal lobe epilepsy (TLE) is the most common type of focal epilepsy characterized by foci in the unilateral temporal lobe grey matters of regions such as the hippocampus. However, it remains unclear how the functional features of white matter are altered in TLE. In the current study, resting-state functional magnetic resonance imaging (fMRI) was performed on 71 left TLE (LTLE) patients, 79 right TLE (RTLE) patients and 47 healthy controls (HC). Clustering analysis was used to identify fourteen white matter networks (WMN). The functional connectivity (FC) was calculated among WMNs and between WMNs and grey matter. Furthermore, the FC laterality of hemispheric WMNs was assessed. First, both patient groups showed decreased FCs among WMNs. Specifically, cerebellar white matter illustrated decreased FCs with the cerebral superficial WMNs, implying a dysfunctional interaction between the cerebellum and the cerebral cortex in TLE. Second, the FCs between WMNs and the ipsilateral hippocampus (grey matter foci) were also reduced in patient groups, which may suggest insufficient functional integration in unilateral TLE. Interestingly, RTLE showed more severe abnormalities of white matter FCs, including links to the bilateral hippocampi and temporal white matter, than LTLE. Taken together, these findings provide functional evidence of white matter abnormalities, extending the understanding of the pathological mechanism of white matter impairments in unilateral TLE.

The use of computational models in the management and prognosis of refractory epilepsy: A critical evaluation

PURPOSE

Drug resistant epilepsy (DRE) affects approximately 30 percent of individuals with epilepsy worldwide. Surgery remains the most effective treatment for individuals with DRE, but referral to surgery is low and only about 60 percent of individuals who undergo surgery experience seizure control postoperatively. The present paper evaluates the evidence for using computational models in the prediction of surgical resection sites and surgical outcomes for patients with DRE.

METHODS

We conducted a search in the Medline data base using the terms "refractory epilepsy", "drug-resistant epilepsy", "surgery", "computational model", and "artificial intelligence". Inclusion: original articles in English and case reports from 2000 to 2020. Reviews were excluded.

RESULTS

Clinical applications of computational models may lead to increased utilisation of surgical services through improving our ability to predict outcomes and by improving surgical outcomes outright. The identification and optimisation of nodes that are crucial for the genesis and propagation of epileptiform activity offers the most promising clinical applications of computational models discussed herein.

CONCLUSION

Advances in computational models may in the future significantly increase the application and efficacy of surgery for patients with DRE by optimising the site and amount of cortex to resect, but more research is needed before it achieves therapeutic utility.

MRI network progression in mesial temporal lobe epilepsy related to healthy brain architecture

We measured MRI network progression in mesial temporal lobe epilepsy (mTLE) patients as a function of healthy brain architecture. Resting-state functional MRI and diffusion-weighted MRI were acquired in 40 unilateral mTLE patients and 70 healthy controls. Data were used to construct region-to-region functional connectivity, structural connectivity, and streamline length connectomes per subject. Three models of distance from the presumed seizure focus in the anterior hippocampus in the healthy brain were computed using the average connectome across controls. A fourth model was defined using regions of transmodal (higher cognitive function) to unimodal (perceptual) networks across a published functional gradient in the healthy brain. These models were used to test whether network progression in patients increased when distance from the anterior hippocampus or along a functional gradient in the healthy brain decreases. Results showed that alterations of structural and functional networks in mTLE occur in greater magnitude in regions of the brain closer to the seizure focus based on healthy brain topology, and decrease as distance from the focus increases over duration of disease. Overall, this work provides evidence that changes across the brain in focal epilepsy occur along healthy brain architecture.

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.

Responsive Neurostimulation in Epilepsy: Wall to Block Seizures or Bridge to Resection?

Mesial Temporal Resection Following Long-Term Ambulatory Intracranial EEG Monitoring With a Direct Brain-Responsive Neurostimulation System

Hirsch LJ, Mirro EA, Salanova V, Witt TC, Drees CN, Brown MG, Lee RW, Sadler TL, Felton EA, Rutecki P, Shin HW, Hadar E, Hegde M, Rao VR, Mnatsakanyan L, Madhavan DS, Zakaria TJ, Liu AA, Heck CN, Greenwood JE, Bigelow JK, Nair DR, Alexopoulos AV, Mackow M, Edwards JC, Sotudeh N, Kuzniecky RI, Gwinn RP, Doherty MJ, Geller EB, Morrell MJ. Epilepsia. 2020;61(3):408-420. doi: 10.1111/epi.16442

Objective:

To describe seizure outcomes in patients with medically refractory epilepsy who had evidence of bilateral mesial temporal lobe (MTL) seizure onsets and underwent MTL resection based on chronic ambulatory intracranial EEG (ICEEG) data from a direct brain-responsive neurostimulator (RNS) system.

Methods:

We retrospectively identified all patients at 17 epilepsy centers with MTL epilepsy who were treated with the RNS system using bilateral MTL leads, and in whom an MTL resection was subsequently performed. Presumed lateralization based on routine presurgical approaches was compared to lateralization determined by RNS system chronic ambulatory ICEEG recordings. The primary outcome was frequency of disabling seizures at last 3-month follow-up after MTL resection compared to seizure frequency 3 months before MTL resection.

Results:

We identified 157 patients treated with the RNS system with bilateral MTL leads due to presumed bitemporal epilepsy. Twenty-five (16%) patients subsequently had an MTL resection informed by chronic ambulatory ICEEG (mean = 42 months ICEEG); follow-up was available for 24 patients. After MTL resection, the median reduction in disabling seizures at last follow-up was 100% (mean: 94%; range: 50%-100%). Nine (38%) patients had exclusively unilateral electrographic seizures recorded by chronic ambulatory ICEEG and all were seizure-free at last follow-up after MTL resection; 8 of 9 continued RNS system treatment. Fifteen (62%) patients had bilateral MTL electrographic seizures, had an MTL resection on the more active side, continued RNS system treatment, and achieved a median clinical seizure reduction of 100% (mean: 90%; range: 50%-100%) at last follow-up, with 8 of 15 seizure-free. For those with more than 1 year of follow-up (N = 21), 15 (71%) patients were seizure-free during the most recent year, including all 8 patients with unilateral onsets and 7 (54%) of 13 patients with bilateral onsets.

Significance:

Chronic ambulatory ICEEG data provide information about lateralization of MTL seizures and can identify additional patients who may benefit from MTL resection.

Temporal lobe epilepsy alters spatio-temporal dynamics of the hippocampal functional network

Mesial temporal lobe epilepsy (TLE) is characterized by transient abnormal electrical activity originating in the hippocampus. The objective of this study was to characterize dynamic spatio-temporal fluctuations in hippocampal network connectivity in mTLE using functional connectivity (FC) mapping in 41 unilateral mTLE patients (28 right, 13 left) and 56 healthy control participants using 3T MRI. Dynamic FC was computed across the scan using sliding 60-s windows. This was compared to static FC computed using the whole 10-min functional MRI scan, and to the variance in the hippocampal functional MRI signal. Four states of healthy hippocampal dynamic FC were identified and compared to TLE patients. TLE patients fluctuated between these four states, but the hippocampus ipsilateral to the seizure focus spent more time in a state distinguished by lower prefrontal and parietal FC than the dominant healthy state. Increased time spent in this state was associated with increased impairment in static FC and increased variance in the hippocampal functional MRI signal. Overall, this work provides evidence that increases in variance in signal fluctuations occurring at the seizure focus in the hippocampus in patients with mTLE may contribute to disruptions in healthy FC network dynamics within an fMRI scan that contribute to decreases in static hippocampal FC. These alterations result in decreased hippocampal connectivity to bilateral prefrontal and parietal regions in TLE which may be related to behavior and cognitive impairments in these patients. Therefore, characterization of an individual patient's hippocampal dynamics at different time scales may provide more specific spatio-temporal profiles of network impairment that may be related to hippocampal dysfunction in TLE.

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.