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BACKGROUND: Optic chiasm invasion by a craniopharyngioma (CP) is exceptional. Surgical treatment of intrachiasmatic CPs associates a high risk of chiasm injury, which should be properly addressed before surgery. CASE DESCRIPTION: We present a 46-year-old woman admitted to the hospital with low visual acuity (0.1 in the right eye and 0.5 in the left) and a severe defect in her visual fields, in addition to headaches, diabetes insipidus, and a long-term depressive disorder. Her visual deficit progressed from a right homonymous temporal inferior quadrantanopia to an almost complete loss of vision in both eyes that only spared the upper nasal quadrants. Brain MRI showed a rounded third ventricle tumor with a potbelly expansion of the optic chiasm, suggesting chiasm invasion by the tumor. Optical coherence tomography (OCT) showed the thinning of the retinal nerve fiber layer (RNFL) in the superior and temporal wedges of the right eye and in the temporal wedge of the left one. The tumor was completely removed by employing a frontotemporal craniotomy and a translamina terminalis approach. Histological analysis showed a squamous-papillary CP. Postoperatively, a significant worsening of the visual defect was evidenced on the perimetry, which was related to a marked RNFL atrophy measured with OCT, as compared to the preoperative study. The poor long-term visual outcome in this patient correlated well with the results of postoperative OCT. CONCLUSIONS: Preoperative analysis of retinal atrophy with optic coherence tomography allows a reliable assessment of the patient's visual outcome in CPs involving the optic chiasm.

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OBJECTIVE: Etomidate mimics some typical epileptic neurophysiological features, such as the appearance of spikes and high frequency oscillations, when it is administrated to epileptic patients. However, little is known about its influence on the underlying cortical network. An assessment of comparable cortical dynamics between seizures and etomidate would allow for a more detailed study of the network parameters underlying the ictal stage by using etomidate as a proxy. The objective of the present work is to show that temporal lobe seizures produce network changes comparable to the ones elicited by etomidate administration. METHODS: Scalp and foramen ovale electrodes (FOE) recordings from nine temporal lobe epilepsy patients were analyzed under the complex network perspective. The clustering coefficients, average path lengths, density of links, modularity and spectral entropy were calculated during the pre-ictal and ictal stages and post-etomidate administration. Etomidate administration produced no seizure in any of the analyzed cases. RESULTS: The density of lines (six of nine patients) and spectral entropy (eight of nine patients) displayed similar behavior to the preictal-ictal transition when etomidate effects altered the epileptic network (FOE+scalp). When considering only the mesial sub-network, changes induced by etomidate perfusion replicated the same type of imbalance observed during the ictal stage in the nine patient's sample and in eight out of nine regarding the preictal stage. Both statistical significance at a level of 1% and size effects, evaluated by using the standardized mean differences, show similar network changes during the preictal-ictal and preictal-etomidate transitions. CONCLUSIONS: Etomidate perfusion in patients with temporal lobe epilepsy induces network changes comparable to the changes resulting from seizures. SIGNIFICANCE: The finding reported here could improve the study of network dynamics during the ictal phase, not only with electrophysiological methods, but also in other cases, such as functional magnetic resonance imaging.

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OBJECTIVE: The current practice under which patients with refractory epilepsy are surgically treated is based mainly on the identification of specific cortical areas, mainly the epileptogenic zone, which is believed to be responsible for generation of seizures. A better understanding of the whole epileptic network and its components and properties is required before more effective and less invasive therapies can be developed. The aim of the present study was to partially characterize the evolution of the functional network during the preictal-ictal transition in partial seizures in patients with temporal lobe epilepsy (TLE). METHODS: Scalp and foramen ovale (FOE) recordings from twenty-two TLE patients were analyzed under the complex network perspective. The density of links, average path length, average clustering coefficient, and modularity were calculated during the preictal and the ictal stages. Both linear-Pearson correlation-and non-linear-phase synchronization-measures were used as proxies of functional connectivity between the electrode locations areas. The transition from one stage to the other was evaluated in the whole network and in the mesial sub-networks. The results were compared with a voltage-dependent measure, namely, the spectral entropy. RESULTS: Changes in the global functional network during the transition from the preictal to the ictal stage show, in the linear case, that in sixteen cases (72.7%) the density of the links increased during the seizure, with a decrease in the average path length in fifteen cases (68.1%). There was also a preictal and ictal imbalance in functional connectivity during both stages (77.2% to 86.3%). The SE dropped during the seizure in 95.4% of the cases, but did not show any tendency towards lateralization. When using the nonlinear measure of functional connectivity, the phase synchronization, similar results were obtained. CONCLUSIONS: In TLE patients, the transition to the ictal stage is accompanied by increasing global synchronization and a more ordered spectral content of the signals, indicated by lower spectral entropy. The interictal connectivity imbalance (lower ipsilateral connectivity) is sustained during the seizure, irrespective of any appreciable imbalance in the spectral entropy of the mesial recordings.

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OBJECTIVE: Neural systems are comprised of interacting units, and relevant information regarding their function or malfunction can be inferred by analyzing the statistical dependencies between the activity of each unit. While correlations and mutual information are commonly used to characterize these dependencies, our objective here is to extend interactions to triplets of variables to better detect and characterize dynamic information transfer. APPROACH: Our approach relies on the measure of interaction information (II). The sign of II provides information as to the extent to which the interaction of variables in triplets is redundant (R) or synergetic (S). Three variables are said to be redundant when a third variable, say Z, added to a pair of variables (X, Y), diminishes the information shared between X and Y. Similarly, the interaction in the triplet is said to be synergetic when conditioning on Z enhances the information shared between X and Y with respect to the unconditioned state. Here, based on this approach, we calculated the R and S status for triplets of electrophysiological data recorded from drug-resistant patients with mesial temporal lobe epilepsy in order to study the spatial organization and dynamics of R and S close to the epileptogenic zone (the area responsible for seizure propagation). MAIN RESULTS: In terms of spatial organization, our results show that R matched the epileptogenic zone while S was distributed more in the surrounding area. In relation to dynamics, R made the largest contribution to high frequency bands (14-100 Hz), while S was expressed more strongly at lower frequencies (1-7 Hz). Thus, applying II to such clinical data reveals new aspects of epileptogenic structure in terms of the nature (redundancy versus synergy) and dynamics (fast versus slow rhythms) of the interactions. SIGNIFICANCE: We expect this methodology, robust and simple, can reveal new aspects beyond pair-interactions in networks of interacting units in other setups with multi-recording data sets (and thus, not necessarily in epilepsy, the pathology we have approached here).

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OBJECTIVE: Interictal synchronization clusters have recently been described in several publications using diverse techniques, including neurophysiological recordings and fMRI, in patients suffering from epilepsy. However, little is known about the role of these hyper-synchronous areas during seizures. In this work, we report an analysis of synchronization clusters jointly with several network measures during seizure activity; we then discuss our findings in the context of prior literature. METHODS: Subdural activity was recorded by electrocorticography (with 60 electrodes placed at temporal and parietal lobe locations) in a patient with temporal lobe epilepsy with partial seizures with and without secondary generalization (SG). Both interictal and ictal activities (during four seizures) were investigated and characterized using local synchronization and complex network methodology. The modularity, density of links, average clustering coefficient, and average path lengths were calculated to obtain information about the dynamics of the global network. Functional connectivity changes during the seizures were compared with the time evolution of highly synchronized areas. RESULTS: Our findings reveal temporal changes in local synchronization areas during seizures and a tight relationship between the cortical locations of these areas and the patterns of their evolution over time. Seizure evolution and SG appear to be driven by two different underlying mechanisms.

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Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.

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Conventional visual analysis and dipole density analysis of magnetoencephalographic data for both spike and low-frequency magnetic activity were compared for presurgical evaluation in temporal lobe epilepsy (TLE) in a sample of 26 drug-resistant operated TLE patients. A series of logistic regression analyses were performed. Dipole density sensitivity was superior to visual localization analysis. Three separated logistic models were calculated for interictal spikes, low-frequency magnetic activity, and the combination of both measures. A combined interictal spike/low-frequency magnetic activity model predicted correctly the operated temporal lobe in all patients. Clear-cut criteria for the probability model are proposed that are valid for 92.3% of cases in the sample. The quantitative approach proposed by this study is an evidence-based model for presurgical evaluation of temporal lobe epilepsy, which improves previous magnetoencephalographic investigations and establishes working clinical criteria for patient evaluation in TLE.

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