Spectrum of motor responses elicited by electrical stimulation of primary motor cortex: A polygraphic study in patients with epilepsy.

OBJECTIVE: To study the neurophysiology of motor responses elicited by electrical stimulation of the primary motor cortex. METHODS: We studied motor responses in four patients undergoing invasive epilepsy monitoring and functional cortical mapping via electrical cortical stimulation using surface EMG electrodes. In addition, polygraphic analysis of intracranial EEG and EMG during bilateral tonic-clonic seizures, induced by cortical stimulation, was performed in two patients. RESULTS: (a) Electrical cortical stimulation: The motor responses were classified as clonic, jittery, and tonic. The clonic responses were characterized by synchronous EMG bursts of agonist and antagonistic muscles, alternating with silent periods. At stimulation frequencies of <20 Hz, EMG bursts were of ≤50 ms duration (Type I clonic). At stimulation frequencies of 20-50 Hz, EMG bursts were of >50 ms duration and had a complex morphology (Type II clonic). Increasing the current intensity at a constant frequency converted clonic responses into jittery and tonic contractions. (b) Bilateral tonic-clonic seizures: The intracranial EEG showed continuous fast spiking activity during the tonic phase along with interference pattern on surface EMG. The clonic phase was characterized by a polyspike-and-slow wave pattern. The polyspikes were time-locked with the synchronous EMG bursts of agonists and antagonists and the slow waves were time-locked with silent periods. INTERPRETATION: These results suggest that epileptic activity involving the primary motor cortex can produce a continuum of motor responses ranging from type I clonic, type II clonic, and tonic responses to bilateral tonic-clonic seizures. This continuum is related to the frequency and intensity of the epileptiform discharges with tonic seizures representing the highest end of the spectrum.

Ictal Lid Movements: Blinks and Lid Saccades.

Two types of lid movements, blinks and lid saccades, have discrete kinematic properties and physiology. These differences are reflected in distinct phenomenology of disorders affecting their neural substrate. Proof of this principle was seen in two patients, one with parietal lobe epilepsy and the other with temporal lobe epilepsy. The lid movements in the patient with parietal lobe epilepsy were rhythmic, yoked, and had a rapid upward component that instantaneously followed a slow downward drift. These cyclic movements strikingly resembled nystagmus, but unlike typical eye nystagmus, the rapid upward component was pathological and seemed to involve a saccadic mechanism. We suggest the terms "ictal lid saccades" or "ictal lid nystagmus" to describe such a phenomenon. In contrast, the patient with temporal lobe epilepsy had ipsilateral lid movements with rapid downward trajectories resembling reflex or spontaneous blinks. The term "ictal blink" is appropriate for this phenomenon.

MaTiLDA: An Integrated Machine Learning and Topological Data Analysis Platform for Brain Network Dynamics.

Topological data analysis (TDA) combined with machine learning (ML) algorithms is a powerful approach for investigating complex brain interaction patterns in neurological disorders such as epilepsy. However, the use of ML algorithms and TDA for analysis of aberrant brain interactions requires substantial domain knowledge in computing as well as pure mathematics. To lower the threshold for clinical and computational neuroscience researchers to effectively use ML algorithms together with TDA to study neurological disorders, we introduce an integrated web platform called MaTiLDA. MaTiLDA is the first tool that enables users to intuitively use TDA methods together with ML models to characterize interaction patterns derived from neurophysiological signal data such as electroencephalogram (EEG) recorded during routine clinical practice. MaTiLDA features support for TDA methods, such as persistent homology, that enable classification of signal data using ML models to provide insights into complex brain interaction patterns in neurological disorders. We demonstrate the practical use of MaTiLDA by analyzing high-resolution intracranial EEG from refractory epilepsy patients to characterize the distinct phases of seizure propagation to different brain regions. The MaTiLDA platform is available at: https://bmhinformatics.case.edu/nicworkflow/MaTiLDA.

Semiology and Neurophysiology of Clonic Seizures: A Report of 39 Patients.

Background and Objectives: Clonic seizures are currently defined as repetitive and rhythmic myoclonic contractions of a specific body part, producing twitching movements at a frequency of 0.2-5 Hz. There are few studies in the literature that have reported a detailed analysis of the semiology, neurophysiology, and lateralizing value of clonic seizures. In this article, we aim to report our findings from a retrospective review of 39 patients. Methods: We identified 39 patients (48 seizures) from our center who had been admitted with clonic seizures between 2016 and 2022. We performed a retrospective review of their video-EEG recordings for semiology and ictal EEG findings. Seventeen patients also had simultaneous surface-EMG (sEMG) electrodes placed on affected body parts, which were analyzed as well. Results: The most common initial affected body parts were face, arm, and hand. In most of the cases, seizures propagated from lower face to upper face and distal hand to proximal arm. The most common seizure-onset zone was the perirolandic region, and the most common EEG seizure pattern was paroxysmal rhythmic monomorphic activity. The lateralizing value for EEG seizure onset to contralateral hemisphere in unilateral clonic seizures (n = 39) was 100%. All seizures recorded with sEMG electrodes demonstrated synchronous brief tetanic contractions of agonists and antagonists, alternating with synchronous silent periods. Arrhythmic clonic seizures were associated with periodic epileptiform discharges on the EEG, whereas rhythmic clonic seizures were associated with paroxysmal rhythmic monomorphic activity. Overall, the most common etiology was cerebrovascular injuries, followed by tumors. Discussion: Clonic seizures are characterized by synchronized brief tetanic contractions of agonist and antagonistic muscles alternating with synchronized silent periods, giving rise to the visible twitching. The most common seizure onset zone is in the perirolandic region, which is consistent with the symptomatogenic zone being in the primary motor area. The lateralizing value of unilateral clonic seizures for seizure onset in the contralateral hemisphere is 100%.

Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy.

The rapid adoption of machine learning (ML) algorithms in a wide range of biomedical applications has highlighted issues of trust and the lack of understanding regarding the results generated by ML algorithms. Recent studies have focused on developing interpretable ML models and establish guidelines for transparency and ethical use, ensuring the responsible integration of machine learning in healthcare. In this study, we demonstrate the effectiveness of ML interpretability methods to provide important insights into the dynamics of brain network interactions in epilepsy, a serious neurological disorder affecting more than 60 million persons worldwide. Using high-resolution intracranial electroencephalogram (EEG) recordings from a cohort of 16 patients, we developed high accuracy ML models to categorize these brain activity recordings into either seizure or non-seizure classes followed by a more complex task of delineating the different stages of seizure progression to different parts of the brain as a multi-class classification task. We applied three distinct types of interpretability methods to the high-accuracy ML models to gain an understanding of the relative contributions of different categories of brain interaction patterns, including multi-focii interactions, which play an important role in distinguishing between different states of the brain. The results of this study demonstrate for the first time that post-hoc interpretability methods enable us to understand why ML algorithms generate a given set of results and how variations in value of input values affect the accuracy of the ML algorithms. In particular, we show in this study that interpretability methods can be used to identify brain regions and interaction patterns that have a significant impact on seizure events. The results of this study highlight the importance of the integrated implementation of ML algorithms together with interpretability methods in aberrant brain network studies and the wider domain of biomedical research.

Characterizing Brain Network Dynamics using Persistent Homology in Patients with Refractory Epilepsy.

Epilepsy is a common serious neurological disorder that affects more than 65 million persons worldwide and it is characterized by repeated seizures that lead to higher mortality and disabilities with corresponding negative impact on the quality of life of patients. Network science methods that represent brain regions as nodes and the interactions between brain regions as edges have been extensively used in characterizing network changes in neurological disorders. However, the limited ability of graph network models to represent high dimensional brain interactions are being increasingly realized in the computational neuroscience community. In particular, recent advances in algebraic topology research have led to the development of a large number of applications in brain network studies using topological structures. In this paper, we build on a fundamental construct of cliques, which are all-to-all connected nodes with a k-clique in a graph G (V, E), where V is set of nodes and E is set of edges, consisting of k-nodes to characterize the brain network dynamics in epilepsy patients using topological structures. Cliques represent brain regions that are coupled for similar functions or engage in information exchange; therefore, cliques are suitable structures to characterize the dynamics of brain dynamics in neurological disorders. We propose to detect and use clique structures during well-defined clinical events, such as epileptic seizures, to combine non-linear correlation measures in a matrix with identification of geometric structures underlying brain connectivity networks to identify discriminating features that can be used for clinical decision making in epilepsy neurological disorder.

Epilepsy-Connect: An Integrated Knowledgebase for Characterizing Alterations in Consciousness State of Pharmacoresistant Epilepsy Patients.

Alterations in consciousness state are a defining characteristic of focal epileptic seizures. Consequently, understanding the complex changes in neurocognitive networks which underpin seizure-induced alterations in consciousness state is important for advancement in seizure classification. Comprehension of these changes are complicated by a lack of data standardization; however, the use of a common terminological system or ontology in a patient registry minimizes this issue. In this paper, we introduce an integrated knowledgebase called Epilepsy-Connect to improve the understanding of changes in consciousness states during focal seizures of pharmacoresistant epilepsy patients. This registry catalogues over 809 seizures from 70 patients at University Hospital's Epilepsy Center who were undergoing stereotactic electroencephalography (SEEG) monitoring as part of an evaluation for surgical intervention. Although Epilepsy-Connect focuses on consciousness states, it aims to enable users to leverage data from an informatics platform to analyze epilepsy data in a streamlined manner. Epilepsy-Connect is available at https://bmhinformatics.case.edu/Epilepsyconnect/login/.

NeuroIntegrative Connectivity (NIC) Informatics Tool for Brain Functional Connectivity Network Analysis in Cohort Studies.

Objective: Brain functional connectivity measures are often used to study interactions between brain regions in various neurological disorders such as epilepsy. In particular, functional connectivity measures derived from high resolution electrophysiological signal data have been used to characterize epileptic networks in epilepsy patients. However, existing signal data formats as well as computational methods are not suitable for complex multi-step methods used for processing and analyzing signal data across multiple seizure events. To address the significant data management challenges associated with signal data, we have developed a new workflow-based tool called NeuroIntegrative Connectivity (NIC) using the Cloudwave Signal Format (CSF) as a common data abstraction model. Method: The NIC compositional workflow-based tool consists of: (1) Signal data processing component for automated pre- processing and generation of CSF files with semantic annotation using epilepsy domain ontology; and (2) Functional network computation component for deriving functional connectivity metrics from signal data analysis across multiple recording channels. The NIC tool streamlines signal data management using a modular software implementation architecture that supports easy extension with new libraries of signal coupling measures and fast data retrieval using a binary search tree indexing structure called NIC-Index. Result and Conclusion: We evaluated the NIC tool by processing and analyzing signal data for 28 seizure events in two patients with refractory epilepsy. The result shows that certain brain regions have high local measure of connectivity, such as total degree, as compared to other regions during ictal events in both patients. In addition, global connectivity measures, which characterize transitivity and efficiency, increase in value during the initial period of the seizure followed by decrease towards the end of seizure. The NIC tool allows users to efficiently apply several network analysis metrics to study global and local changes in epileptic networks in patient cohort studies.

Episodic gaze deviation in multiple sclerosis - Versive seizures or oculogyric crises?

Ictal gaze deviation and oculogyric crisis (OGC) can show identical clinical manifestations. We report a case of repeated drug induced OGCs in a 38 year old patient with secondary progressive multiple sclerosis. He was referred to our center for treatment of "intractable" epilepsy manifesting as episodic eye and head deviations with apparent unresponsiveness. In the epilepsy monitoring unit, ten typical spells were captured without epileptiform electroencephalographic correlates, but we discovered chronic exposure to metoclopramide. A diagnosis of OGC was suspected and Metoclopramide was stopped. This robustly improved the frequency of his spells. In a setting of usage of antidopaminergic medications and/or pontomesencephalic lesions, a low threshold should be kept for the diagnosis of oculogyric crisis, thus avoiding seizure diagnoses and inappropriate treatment of the phenomenon. Video-EEG monitoring is essential for teasing apart epilepsy and OGC.