Giridhar P. Kalamangalam

Intravenous ketamine for the treatment of refractory status epilepticus: A retrospective multicenter study

To examine patterns of use, efficacy, and safety of intravenous ketamine for the treatment of refractory status epilepticus (RSE).

Stereotactic laser ablation of epileptogenic periventricular nodular heterotopia

Periventricular nodular heterotopia (PVNH) is a neuronal migrational disorder often associated with pharmacoresistant epilepsy (PRE). Resective surgery for PVNH is limited by its deep location, and the overlying eloquent cortex or white matter. Stereotactic MR guided laser interstitial thermal therapy (MRgLITT) has recently become available for controlled focal ablation, enabling us to target these lesions. We here demonstrate the novel application and techniques for the use of MRgLITT in the management of PVNH epilepsy. Comprehensive presurgical evaluation, including intracranial EEG monitoring in two patients revealed the PVNH to be crucially involved in their PRE. We used MRgLITT to maximally ablate the PVNH in both cases. In the first case, seizure medication adjustment coupled with PVNH ablation, and in the second, PVNH ablation in addition to temporal lobectomy rendered the patient seizure free. A transient visual deficit occurred following ablation in the second patient. MRgLITT is a promising minimally invasive technique for ablation of epileptogenic PVNH, a disease not generally viewed as surgically treatable epilepsy. We also show here the feasibility of applying this technique through multiple trajectories and to create lesions of complex shapes. The broad applicability and long term efficacy of MRgLITT need to be elaborated further.

Temporal lobe white matter asymmetry and language laterality in epilepsy patients

Recent studies using diffusion tensor imaging (DTI) have advanced our knowledge of the organization of white matter subserving language function. It remains unclear, however, how DTI may be used to predict accurately a key feature of language organization: its asymmetric representation in one cerebral hemisphere. In this study of epilepsy patients with unambiguous lateralization on Wada testing (19 left and 4 right lateralized subjects; no bilateral subjects), the predictive value of DTI for classifying the dominant hemisphere for language was assessed relative to the existing standard-the intra-carotid Amytal (Wada) procedure. Our specific hypothesis is that language laterality in both unilateral left- and right-hemisphere language dominant subjects may be predicted by hemispheric asymmetry in the relative density of three white matter pathways terminating in the temporal lobe implicated in different aspects of language function: the arcuate (AF), uncinate (UF), and inferior longitudinal fasciculi (ILF). Laterality indices computed from asymmetry of high anisotropy AF pathways, but not the other pathways, classified the majority (19 of 23) of patients using the Wada results as the standard. A logistic regression model incorporating information from DTI of the AF, fMRI activity in Brocas area, and handedness was able to classify 22 of 23 (95.6%) patients correctly according to their Wada score. We conclude that evaluation of highly anisotropic components of the AF alone has significant predictive power for determining language laterality, and that this markedly asymmetric distribution in the dominant hemisphere may reflect enhanced connectivity between frontal and temporal sites to support fluent language processes. Given the small sample reported in this preliminary study, future research should assess this method on a larger group of patients, including subjects with bi-hemispheric dominance.

Neuroimaging and neurophysiology of periodic lateralized epileptiform discharges: Observations and hypotheses

Summary:  Purpose: We assessed neuroimaging lesion type and distribution in patients with periodic lateralized epileptiform discharges (PLEDs), with a view to identifying electrographic differences between PLEDs associated with differing lesion locations. Our observations led us to consider a conceptual synthesis between PLEDs and periodic complexes (PCs).

Intracranial evaluation and laser ablation for epilepsy with periventricular nodular heterotopia

Surgical treatment of focal epilepsy in the presence of periventricular nodular heterotopia (PVNH) poses a challenge, as the relative roles of the nodular tissue and the overlying cortex in the generation of seizures can be complex and variable. Here, we review the literature on chronic invasive EEG recordings in humans with this substrate and present two illustrative cases from our practice. We found that while inter-ictal spiking from nodules is common, clinical seizures rarely arise solely from nodular tissue. More typically, ictal onset is simultaneous with overlying neocortex or mesial temporal structures. Surgical outcome is more favorable in cases with unilateral (as opposed to bilateral) PVNH, and when a substantial or complete ablation of PVNH is performed. In rare cases, nodular ablation alone may be sufficient, as may be completed by MRI-guided laser interstitial thermal therapy. The mechanism(s) by which PNVH interacts with overlying cortex are not fully understood, but we suggest that PVNH either orchestrates or amplifies local network epileptogenicity. At present, invasive recordings with penetrating depth electrodes are required prior to surgical therapy, as illustrated in our cases.

Proposal: Different types of alteration and loss of consciousness in epilepsy

There are at least five types of alterations of consciousness that occur during epileptic seizures: auras with illusions or hallucinations, dyscognitive seizures, epileptic delirium, dialeptic seizures, and epileptic coma. Each of these types of alterations of consciousness has a specific semiology and a distinct pathophysiologic mechanism. In this proposal we emphasize the need to clearly define each of these alterations/loss of consciousness and to apply this terminology in semiologic descriptions and classifications of epileptic seizures. The proposal is a consensus opinion of experienced epileptologists, and it is hoped that it will lead to systematic studies that will allow a scientific characterization of the different types of alterations/loss of consciousness described in this article.

Sensitivity of quantitative EEG for seizure identification in the intensive care unit

Objective: To evaluate the sensitivity of quantitative EEG (QEEG) for electrographic seizure identification in the intensive care unit (ICU). Methods: Six-hour EEG epochs chosen from 15 patients underwent transformation into QEEG displays. Each epoch was reviewed in 3 formats: raw EEG, QEEG + raw, and QEEG-only. Epochs were also analyzed by a proprietary seizure detection algorithm. Nine neurophysiologists reviewed raw EEGs to identify seizures to serve as the gold standard. Nine other neurophysiologists with experience in QEEG evaluated the epochs in QEEG formats, with and without concomitant raw EEG. Sensitivity and false-positive rates (FPRs) for seizure identification were calculated and median review time assessed. Results: Mean sensitivity for seizure identification ranged from 51% to 67% for QEEG-only and 63%–68% for QEEG + raw. FPRs averaged 1/h for QEEG-only and 0.5/h for QEEG + raw. Mean sensitivity of seizure probability software was 26.2%–26.7%, with FPR of 0.07/h. Epochs with the highest sensitivities contained frequent, intermittent seizures. Lower sensitivities were seen with slow-frequency, low-amplitude seizures and epochs with rhythmic or periodic patterns. Median review times were shorter for QEEG (6 minutes) and QEEG + raw analysis (14.5 minutes) vs raw EEG (19 minutes; p = 0.00003). Conclusions: A panel of QEEG trends can be used by experts to shorten EEG review time for seizure identification with reasonable sensitivity and low FPRs. The prevalence of false detections confirms that raw EEG review must be used in conjunction with QEEG. Studies are needed to identify optimal QEEG trend configurations and the utility of QEEG as a screening tool for non-EEG personnel. Classification of evidence review: This study provides Class II evidence that QEEG + raw interpreted by experts identifies seizures in patients in the ICU with a sensitivity of 63%–68% and FPR of 0.5 seizures per hour.

Myelopathy from intracranial dural arteriovenous fistula

Dural arteriovenous fistulas arising intracranially are an uncommon cause of progressive myelopathy. This report is of a patient in whom the diagnosis of the condition was confounded by coexisting small vessel cerebrovascular disease.

Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. DI, an information theoretic quantity, is a general metric to infer causal connectivity between time series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferre using the model-based and data-driven DI estimators, respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against the visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step toward developing novel nonsurgical treatments for epilepsy.

Online Bayesian change point detection algorithms for segmentation of epileptic activity

Epilepsy is a dynamic disease in which the brain transitions between different states. In this paper, we focus on the problem of identifying the time points, referred to as change points, where the transitions between these different states happen. A Bayesian change point detection algorithm that does not require the knowledge of the total number of states or the parameters of the probability distribution modeling the activity of epileptic brain in each of these states is developed in this paper. This algorithm works in online mode making it amenable for real-time monitoring. To reduce the quadratic complexity of this algorithm, an approximate algorithm with linear complexity in the number of data points is also developed. Finally, we use these algorithms on ECoG recordings of an epileptic patient to locate the change points and determine segments corresponding to different brain states.