Focal to bilateral tonic-clonic seizures are associated with lower quality of life, higher risk of seizure-related injuries, increased chance of sudden unexpected death, and unfavourable treatment outcomes. Achieving greater understanding of their underlying circuitry offers better opportunity to control these seizures. Towards this goal, we provide a network science perspective of the interactive pathways among basal ganglia, thalamus and cortex, to explore the imprinting of secondary seizure generalization on the mesoscale brain network in temporal lobe epilepsy. Specifically, we parameterized the functional organization of both the thalamocortical network and the basal ganglia–thalamus network with resting state functional MRI in three groups of patients with different focal to bilateral tonic-clonic seizure histories. Using the participation coefficient to describe the pattern of thalamocortical connections among different cortical networks, we showed that, compared to patients with no previous history, those with positive histories of focal to bilateral tonic-clonic seizures, including both remote (none for >1 year) and current (within the past year) histories, presented more uniform distribution patterns of thalamocortical connections in the ipsilateral medial-dorsal thalamic nuclei. As a sign of greater thalamus-mediated cortico-cortical communication, this result comports with greater susceptibility to secondary seizure generalization from the epileptogenic temporal lobe to broader brain networks in these patients. Using interregional integration to characterize the functional interaction between basal ganglia and thalamus, we demonstrated that patients with current history presented increased interaction between putamen and globus pallidus internus, and decreased interaction between the latter and the thalamus, compared to the other two patient groups. Importantly, through a series of ‘disconnection’ simulations, we showed that these changes in interactive profiles of the basal ganglia–thalamus network in the current history group mainly depended upon the direct but not the indirect basal ganglia pathway. It is intuitively plausible that such disruption in the striatum-modulated tonic inhibition of the thalamus from the globus pallidus internus could lead to an under-suppressed thalamus, which in turn may account for their greater vulnerability to secondary seizure generalization. Collectively, these findings suggest that the broken balance between basal ganglia inhibition and thalamus synchronization can inform the presence and effective control of focal to bilateral tonic-clonic seizures. The mechanistic underpinnings we uncover may shed light on the development of new treatment strategies for patients with temporal lobe epilepsy.

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Temporal lobe epilepsy tends to reshape the language system causing maladaptive reorganization that can be characterized by task-based functional MRI, and eventually can contribute to surgical decision making processes. However, the dynamic interacting nature of the brain as a complex system is often neglected, with many studies treating the language system as a static monolithic structure. Here, we demonstrate that as a specialized and integrated system, the language network is inherently dynamic, characterized by rich patterns of regional interactions, whose transient dynamics are disrupted in patients with temporal lobe epilepsy. Specifically, we applied tools from dynamic network neuroscience to functional MRI data collected from 50 temporal lobe epilepsy patients and 30 matched healthy controls during performance of a verbal fluency task, as well as during rest. By assigning 16 language-related regions into four subsystems (i.e. bilateral frontal and temporal), we observed regional specialization in both the probability of transient interactions and the frequency of such changes, in both healthy controls and patients during task performance but not rest. Furthermore, we found that both left and right temporal lobe epilepsy patients displayed reduced interactions within the left frontal 'core' subsystem compared to the healthy controls, while left temporal lobe epilepsy patients were unique in showing enhanced interactions between the left frontal 'core' and the right temporal subsystems. Also, both patient groups displayed reduced flexibility in the transient interactions of the left temporal and right frontal subsystems, which formed the 'periphery' of the language network. Importantly, such group differences were again evident only during task condition. Lastly, through random forest regression, we showed that dynamic reconfiguration of the language system tracks individual differences in verbal fluency with superior prediction accuracy compared to traditional activation-based static measures. Our results suggest dynamic network measures may be an effective biomarker for detecting the language dysfunction associated with neurological diseases such as temporal lobe epilepsy, specifying both the type of neuronal communications that are missing in these patients and those that are potentially added but maladaptive. Further advancements along these lines, transforming how we characterize and map language networks in the brain, have a high probability of altering clinical decision making in neurosurgical centres.10.1093/brain/awy042_video1awy042media15754656112001.

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OBJECTIVEDespite numerous imaging studies highlighting the importance of the thalamus in a patient’s surgical prognosis, human electrophysiological studies involving the limbic thalamic nuclei are limited. The objective of this study was to evaluate the safety and accuracy of robot-assisted stereotactic electrode placement in the limbic thalamic nuclei of patients with suspected temporal lobe epilepsy (TLE).METHODSAfter providing informed consent, 24 adults with drug-resistant, suspected TLE undergoing evaluation with stereoelectroencephalography (SEEG) were enrolled in the prospective study. The trajectory of one electrode planned for clinical sampling of the operculoinsular cortex was modified to extend it to the thalamus, thereby preventing the need for additional electrode placement for research. The anterior nucleus of the thalamus (ANT) (n = 13) and the medial group of thalamic nuclei (MED) (n = 11), including the mediodorsal and centromedian nuclei, were targeted. The postimplantation CT scan was coregistered to the preoperative MR image, and Morel’s thalamic atlas was used to confirm the accuracy of implantation.RESULTSTen (77%) of 13 patients in the ANT group and 10 (91%) of 11 patients in the MED group had electrodes accurately placed in the thalamic nuclei. None of the patients had a thalamic hemorrhage. However, trace asymptomatic hemorrhages at the cortical-level entry site were noted in 20.8% of patients, who did not require additional surgical intervention. SEEG data from all the patients were interpretable and analyzable. The trajectories for the ANT implant differed slightly from those of the MED group at the entry point—i.e., the precentral gyrus in the former and the postcentral gyrus in the latter.CONCLUSIONSUsing judiciously planned robot-assisted SEEG, the authors demonstrate the safety of electrophysiological sampling from various thalamic nuclei for research recordings, presenting a technique that avoids implanting additional depth electrodes or compromising clinical care. With these results, we propose that if patients are fully informed of the risks involved, there are potential benefits of gaining mechanistic insights to seizure genesis, which may help to develop neuromodulation therapies.

BACKGROUND AND PURPOSE:The frequency of seizures is an important factor that can alter functional brain connectivity. Analysis of this factor in patients with epilepsy is complex because of disease-and medication-induced confounders. Because patients with hot-water epilepsy generally are not on long-term drug therapy, we used seed-based connectivity analysis in these patients to assess connectivity changes associated with seizure frequency without confounding from antiepileptic drugs.

Ripples and spikes generated in the left temporal neocortex are associated with impaired verbal episodic memory encoding. Although physiological and pathological ripple oscillations were not distinguished during cognitive tasks, our results show an association of undifferentiated ripples with impaired encoding. The effect was sometimes specific to regions outside the SOZ, suggesting that widespread effects of epilepsy outside the SOZ may contribute to cognitive impairment.

Objectives Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. Methods Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain "rsfMRI epilepsy networks." Results SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebellothalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs. Conclusions IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these "rsfMRI epilepsy networks" could reflect epileptogenesis in TLE. Key Points• ICA of resting-state fMRI carries disease-specific information about epilepsy.• Machine learning can classify these components with 97.5% accuracy.• "Subject-specific epilepsy networks" could quantify "epileptogenesis" in vivo.