High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom

We evaluate whether interictal spikes, epileptiform HFOs and their co-occurrence (Spike + HFO) were included in the resection area with respect to seizure outcome. We also characterise the relationship between high frequency oscillations (HFOs) and propagating spikes. We analysed intracranial EEG of 20 patients that underwent resective epilepsy surgery. The co-occurrence of ripples and fast ripples was considered an HFO event; the co-occurrence of an interictal spike and HFO was considered a Spike + HFO event. HFO distribution and spike onset were compared in cases of spike propagation. Accuracy in predicting seizure outcome was 85% for HFO, 60% for Spikes, and 79% for Spike + HFO. Sensitivity was 57% for HFO, 71% for Spikes and 67% for Spikes + HFO. Specificity was 100% for HFO, 54% for Spikes and 85% for Spikes + HFO. In 2/2 patients with spike propagation, the spike onset included the HFO area. Combining interictal spikes with HFO had comparable accuracy to HFO. In patients with propagating spikes, HFO rate was maximal at the onset of spike propagation

Human Intracranial High Frequency Oscillation Detection Using Time Frequency Analysis and Its Relation to the Seizure Onset Zone

One third of the patients diagnosed with focal epilepsy do not respond to antiepileptic drugs. For these patients the possible diagnosis options to give seizure freedom or at least reduce seizure frequencies significantly would be surgical resection or seizure interrupting implantable devices. The success of these procedures depends on accurate detection of the region causing seizure also known as epileptic zone. This requires detail pre-surgical evaluation including Invasive Video Electroencephalographic Monitoring (IVEM). The resulting great volume of intracranial Electroencephalography (iEEG) signal is visually examined by an expert epileptologist which can be time consuming, extremely complex, and not always effective. We have introduced an automated method to help the epileptologist analyze the iEEG signals. Literature suggest that signals recorded from brain regions subject to seizure activity produce a short durational high gamma ripple activity in the iEEG called High Frequency Oscillations (HFOs). The algorithm presented in this thesis uses an automated time-frequency space analysis method to detect HFOs and distinguish them from high frequency artifacts. As HFOs are short-lived high frequency oscillations, the time-frequency space analysis method chosen should have good time and frequency resolution capabilities. The Stockwell transform was used for this purpose which is a variable window version of the Short Time Fourier Transform (STFT). We have modified the detection algorithm to analyze the multi-channel iEEG data obtained from patients monitored at the Spectrum Health Epilepsy Monitoring Unit (EMU) and found that the electrode site recordings exhibiting higher HFO rate are within the Seizure Onset Zone (SOZ) determined by visual examination of the iEEG recordings by the epileptologist. These electrodes also continue to show higher HFO rate throughout the entire study. The HFO analysis presented in this thesis suggests that HFO detection and identification may be used to reduce IVEM monitoring time by aiding the neurosurgeon delineating the epileptic zone in relatively shorter time. This will lead to better surgical outcome or succesful implantation of the seizure intervention devices

Advanced Invasive Neurophysiological Methods to Aid Decision Making in Paediatric Epilepsy Surgery

For patients with drug-resistant focal epilepsy, surgery is the most effective treatment to attain seizure freedom. Intracranial electroencephalogram investigations succeed in defining the seizure onset zone (SOZ) where non-invasive methods fail to identify a single seizure generator. However, resection of the SOZ does not always lead to a surgical benefit and, in addition, eloquent functions like language might be compromised. The aim of this thesis was to use advanced invasive neurophysiological methods to improve pre-surgical planning in two ways. The first aim was to improve delineation of the pathological tissue, the SOZ using novel quantitative neurophysiological biomarkers: high gamma activity (80–150Hz) phase-locked to low frequency iEEG discharges (phase-locked high gamma, PLHG) and high frequency oscillations called fast ripples (FR, 250–500Hz). Resection of contacts containing these markers were recently reported to lead to an improved seizure outcome. The current work shows the first replication of the PLHG metric in a small adult pilot study and a larger paediatric cohort. Furthermore, I tested whether surgical removal of PLHG- and/or FR-generating brain areas resulted in better outcome compared to the current clinical SOZ delineation. The second aim of this work was to aid delineation of eloquent language cortex. Invasive event-related potentials (iERP) and spectral changes in the beta and gamma frequency bands were used to determine cortical dynamics during speech perception and production across widespread brain regions. Furthermore, the relationship between these cortical dynamics and the relationship to electrical stimulation responses was explored. For delineation of pathological tissue, the combination of FRs and SOZ proved to be a promising biomarker. Localising language cortex showed the highest level of activity around the perisylvian brain regions with a significantly higher occurrence rate of iERPs compared to spectral changes. Concerning electrical stimulation mapping beta and high gamma frequency bands represented the most promising markers

Temporal Characteristics of High-Frequency Oscillations as a Biomarker of Human Epilepsy

Epilepsy is a debilitating neurological disorder characterized by recurrent spontaneous seizures. While seizures themselves adversely affect physiological function for short time periods relative to normal brain states, their cumulative impact can significantly decrease patient quality of life in myriad ways. For many, anti-epileptic drugs are effective first-line therapies. One third of all patients do not respond to chemical intervention, however, and require invasive resective surgery to remove epileptic tissue. While this is still the most effective last-line treatment, many patients with ‘refractory’ epilepsy still experience seizures afterward, while some are not even surgical candidates. Thus, a significant portion of patients lack further recourse to manage their seizures – which additionally impacts their quality of life. High-frequency oscillations (HFOs) are a recently discovered electrical biomarker with significant clinical potential in refractory human epilepsy. As a spatial biomarker, HFOs occur more frequently in epileptic tissue, and surgical removal of areas with high HFO rates can result in improved outcomes. There is also limited preliminary evidence that HFOs change prior to seizures, though it is currently unknown if HFOs function as temporal biomarkers of epilepsy and imminent seizure onset. No such temporal biomarker has ever been identified, though if it were to exist, it could be exploited in online seizure prediction algorithms. If these algorithms were clinically implemented in implantable neuromodulatory devices, improvements to quality of life for refractory epilepsy patients might be possible. Thus, the overall aim of this work is to investigate HFOs as potential temporal biomarkers of seizures and epilepsy, and further to determine whether their time-varying properties can be exploited in seizure prediction. In the first study we explore population-level evidence for the existence of this temporal effect in a large clinical cohort with refractory epilepsy. Using sophisticated automated HFO detection and big-data processing techniques, a continuous measure of HFO rates was developed to explore gradual changes in HFO rates prior to seizures, which were analyzed in aggregate to assess their stereotypical response. These methods resulted in the identification of a subset of patients in whom HFOs from epileptic tissue gradually increased before seizures. In the second study, we use machine learning techniques to investigate temporal changes in HFO rates within individuals, and to assess their potential usefulness in patient-specific seizure prediction. Here, we identified a subset of patients whose predictive models sufficiently differentiated the preictal (before seizure) state better than random chance. In the third study, we extend our prediction framework to include the signal properties of HFOs. We explore their ability to improve the identification of preictal periods, and additionally translate their predictive models into a proof-of-concept seizure warning system. For some patients, positive results from this demonstration show that seizure prediction using HFOs could be possible. These studies overall provide convincing evidence that HFOs can change in measurable ways prior to seizure start. While this effect was not significant in some individuals, for many it enabled seizures to be predicted above random chance. Due to data limitations in overall recording duration and number of seizures captured, these findings require further validation with much larger high-density intracranial EEG datasets. Still, they provide a preliminary framework for the eventual use of HFOs in patient-specific seizure prediction with the potential to improve the lives of those with refractory epilepsy.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168079/1/jaredmsc_1.pd

Source-sink connectivity: A novel interictal EEG marker of the epileptic brain network

Epilepsy affects over 60 million people worldwide. Epilepsy diagnosis depends on abnormalities in scalp electroencephalography (EEG) signals but their presence varies from 29-55%, resulting in a delayed diagnosis. Additionally, artifacts mimicking abnormalities and conditions imitating epileptic seizures contribute to a misdiagnosis rate of 30%. Antiepileptic drugs (AEDs) are the mainstay of epilepsy treatment, but around 30% of patients do not respond to AEDs. Surgical treatment is a hopeful alternative but outcomes depend on precise identification of the epileptogenic zone (EZ), the brain region(s) where seizures originate, and success rates range from 20-80%. Localization of the EZ requires visual inspection of intracranial EEG (iEEG) recordings during seizures which is costly and time-consuming and, in the end, clinicians ignore most of the data captured. Diagnosis and management of epilepsy rely on detecting sporadic EEG signatures. Thus, there is a great need to more quickly and accurately identify the underlying cause and location of seizures in the brain. We developed and tested the source-sink index (SSI) as an interictal (between seizures) EEG marker of epileptogenic activity. We hypothesized that seizures are suppressed when the EZ is inhibited by neighboring regions. We developed an algorithm that identifies two groups of nodes from the EEG network: those inhibiting their neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, dynamical network models were estimated from EEG data and their connectivity properties revealed top sources and sinks in the network. We tested and validated a twofold application of SSI, as: i) an iEEG marker of the EZ, and ii) a scalp EEG marker of epilepsy. We found that SSI highly agreed with the annotated EZ in successful outcome patients but identified untreated regions in failure patients. Further, SSI outperformed high frequency oscillations, a frequently proposed interictal EZ marker, in predicting surgical outcomes. When used to predict diagnostic outcomes, SSI showed significant improvement over the gold standard's reported sensitivity and specificity. Our results suggest that SSI captures the characteristics of regions responsible for seizure initiation. As such, it is a promising marker of epileptogenicity that could significantly improve the speed and outcomes of epilepsy management and diagnosis

Learning more with less data using domain-guided machine learning: the case for health data analytics

The United States is facing a shortage of neurologists with severe consequences: a) average wait-times to see neurologists are increasing, b) patients with chronic neurological disorders are unable to receive diagnosis and care in a timely fashion, and c) there is an increase in neurologist burnout leading to physical and emotional exhaustion. Present-day neurological care relies heavily on time-consuming visual review of patient data (e.g., neuroimaging and electroencephalography (EEG)), by expert neurologists who are already in short supply. As such, the healthcare system needs creative solutions that can increase the availability of neurologists to patient care. To meet this need, this dissertation develops a machine-learning (ML)-based decision support framework for expert neurologists that focuses the experts’ attention to actionable information extracted from heterogeneous patient data and reduces the need for expert visual review. Specifically, this dissertation introduces a novel ML framework known as domain-guided machine learning (DGML) and demonstrates its usefulness by improving the clinical treatments of two major neurological diseases, epilepsy and Alzheimer’s disease. In this dissertation, the applications of this framework are illustrated through several studies conducted in collaboration with the Mayo Clinic, Rochester, Minnesota. Chapters 3, 4, and 5 describe the application of DGML to model the transient abnormal discharges in the brain activity of epilepsy patients. These studies utilized the intracranial EEG data collected from epilepsy patients to delineate seizure generating brain regions without observing actual seizures; whereas, Chapters 6, 7, 8, and 9 describe the application of DGML to model the subtle but permanent changes in brain function and anatomy, and thereby enable the early detection of chronic epilepsy and Alzheimer’s disease. These studies utilized the scalp EEG data of epilepsy patients and two population-level multimodal imaging datasets collected from elderly individuals

Détection automatique multi-échelle et de grande envergure d'oscillations intracérébrales pathologiques dans l'épilepsie par réseaux de neurones artificiels

Environ un tiers des patients épileptiques sont résistants aux médicaments. La seule solution pour les guérir est de retirer la zone cérébrale à l'origine des crises, appelée zone épileptogène (ZE). Pour localiser cette zone, il est parfois nécessaire des mener des explorations par stéréo-électroencéphalographie (SEEG). L'analyse du signal EEG par les neurologues est une étape déterminante du diagnostic, mais la quantité de données générée est colossale. Ainsi, seule une petite partie des enregistrements peut être analysée par les équipes médicales qui se concentrent principalement sur l'activité durant les crises et celle juste autour. Pour caractériser l'étendue et la dynamique du réseau épileptogène, les neurologues étudient aussi des marqueurs intercritiques. Mais certains de ces biomarqueurs sont strictement invisibles à l'œil nu. Le premier objectif de ce travail de thèse interdisciplinaire consistait à établir de nouvelles méthodes pour détecter efficacement et automatiquement les marqueurs intercritiques, à savoir les pointes épileptiques intercritiques (PEIs) et en particulier les fast ripples (FRs). Le second objectif visait à définir et décrire l'intérêt d'enregistrements des marqueurs physiopathologiques de l'épilepsie par l'intermédiaire de micro-électrodes, alors que la plupart des études jusqu'à présent utilisaient des macro-électrodes classiques. Enfin, le troisième objectif était focalisé sur les FRs, avec pour idée de mieux comprendre leur origine, leur émergence et leur implication dans la pathologie. Nos équipes utilisent des électrodes hybrides permettant un enregistrement multi échelle du signal cérébral des patients. Ces électrodes sont constituées de macro-canaux permettant d'enregistrer l'activité de larges populations neuronales et de micro-canaux capables de capturer des signaux plus focaux, pouvant aller jusqu'à l'échelle du neurone unitaire. Nous avons construit un détecteur automatique de PEIs basé sur une nouvelle méthode de traitement du signal que nous avons baptisée Convolutional Kernel Density Estimation (CKDE). Nous avons également élaboré un détecteur automatique de FRs basé sur une approche écologique en trois étapes, imitant le travail du neurologue. Tous ces outils ont été incorporés à des interfaces graphiques utilisateurs (GUI) combinant les différentes fonctionnalités pour en permettre l'utilisation facile et efficiente. La détection des PEIs par CKDE offre la preuve de concept qu'une analyse orientée pixels de l'activité EEG peut être utilisée comme stratégie pour détecter des marqueurs intercritiques. Nous avons évalué cette méthode sur 10 minutes d'enregistrements chez un patient. Quinze PEIs ont été détectées automatiquement parmi lesquelles 13 vrais positifs et 2 faux positifs. Nos résultats principaux concernent toutefois la détection des FRs qui auraient à ce jour le plus grand potentiel dans le diagnostic des épilepsies pharmacorésistantes. Pour entraîner le CNN qui est une pièce maîtresse de notre détecteur, nous avons constitué une base de données de 4 954 FRs détectés manuellement chez 13 patients. Ce détecteur de FRs a été incorporé au logiciel que nous avons imaginé et créé, baptisé Ladybird, utilisé chez 29 patients pour détecter et traiter plusieurs milliers de FRs. Les avancées techniques et théoriques réalisées au cours de ce travail de thèse nous permettent d'envisager une utilisation à grande échelle de nos outils. Notre objectif est que les équipes médicales puissent en bénéficier directement, dans leur routine diagnostic. Un brevet a été déposé en vue d'un processus d'industrialisation.Almost a third of epileptic patients are resistant to medication. The only way to cure them is to remove the area of the brain that causes the seizures, called the epileptogenic zone (EZ). To locate this area, it is sometimes necessary to carry out stereo-electroencephalography (SEEG) investigations. SEEG consists of implanting intracerebral electrodes in the patient, who remains in hospital for about ten to fifteen days. During this period, the patient's intracerebral activity is continuously recorded on more than a hundred recording channels distributed in the brain structures suspected of being involved in the epileptogenic network. The analysis of the EEG signal by neurologists is a crucial step in the diagnosis, but the amount of data generated is tremendous. As a result, only a small fraction of the recordings can be analyzed by medical teams, who focus mainly on activity during and immediately surrounding seizures. To characterise the extent and dynamics of the epileptogenic network, neurologists also study interictal markers. But some of these biomarkers are impossible to detect manually. The first objective of this interdisciplinary thesis work was to establish new methods to efficiently and automatically detect intercritical markers, namely interictal epileptic discharges (IEDs) and in particular fast ripples (FRs). The second objective was to define and describe the interest of recording pathophysiological markers of epilepsy using micro-electrodes, whereas most studies until now used classical macro-electrodes. Finally, the third objective was focused on FRs, with the idea to better understand their origin, emergence and involvement in the pathology. Our teams use hybrid electrodes that allow for a multi-scale recording of the brain signal of patients. These electrodes are made up of macro-channels allowing the recording of the activity of large neuronal populations and micro-channels capable of capturing much more focal signals, down to the scale of single neuron activity. We have built an automatic IED detector based on a new method of processing the image-transformed signal using a technique we call Convolutional Kernel Density Estimation (CKDE). We also developed an automatic FR detector based on a three-step, CNN-based, ecological approach, mimicking the work of the neurologist. All these tools have been incorporated into graphical user interfaces (GUIs) that combine the different functionalities for easy and efficient use. The detection of IEDs by CKDE offers proof of concept that a pixel-oriented analysis of EEG activity can be used as a strategy to detect interictal markers. We evaluated this method on 10 minutes of recordings in a patient. Fifteen IEDs were automatically detected, of which 13 were true positives and 2 false positives. However, our main results concern the detection of FRs, which would have the greatest potential in the diagnosis of drug-resistant epilepsies. To train the CNN, which is a key component of our detector, we built a database of 4,954 manually detected FRs in 13 patients at both the EEG-macro and the EEG-micro scales. This multi-scale FR detector was incorporated into the software we designed, called Ladybird, which was used in 29 patients to detect and treat several thousand FRs. The technical and theoretical advances made during this thesis allow us to consider a large-scale use of our tools. Our objective is that medical teams can benefit directly from them, in their diagnostic routine. A patent has been filed in view of an industrialization process

Temporal coordination of neuronal activity underlies human memory and learning

Memory-related disorders, such as Alzheimer’s disease and dementia, are devastating and often irreparable given our limited knowledge of how to effectively treat them. Animal studies have made significant advances in identifying neural correlates of memory, but in order to develop better interventions for memory loss, we need a deeper understanding of the neural basis of memory in the human brain. The main focus of my research is examining large-scale electrophysiological correlates of memory and learning in humans. In my studies, I recorded local field potential (LFP) data directly from the brains of neurosurgical patients performing memory tasks. First, in Chapter 2, I investigated the prevalence of sharp-wave ripples—synchronous high-frequency bursts of LFP activity—in the human hippocampus and cortex. I found that spectral characteristics of detected ripples closely matched those of other previously described high-frequency patterns in the human brain, thus raising important considerations for the detection and definition of ripple-like activity in humans. For my second study, in Chapter 3, I examined the impact of scopolamine, a cholinergic blocker, in the human hippocampal area during episodic memory. I found that the memory impairment caused by scopolamine was coupled to disruptions of both the amplitude and phase alignment of theta oscillations (2-10 Hz) during encoding. These findings suggest that cholinergic circuits support memory by coordinating the temporal dynamics of theta oscillations. Finally, in Chapter 4, I explored how brain oscillations in the medial temporal lobe (MTL) support learning. I found that subjects’ accuracy in a spatial memory task improved significantly within and across sessions, and that these short- and long-term learning effects were predicted by greater theta synchrony. My research translates important memory- and learning-related signals from animal studies, and extends those findings by revealing spectral patterns that are specifically relevant to humans. Together, my studies point to a key electrophysiological phenomenon underlying memory and learning in humans: the synchrony of neuronal activity in the brain. In particular, my results suggest that the temporal coordination of neuronal activity offered by brain oscillations, especially those in the theta frequency band, is vital for successful memory and learning. These findings expand our mechanistic understanding of the neurophysiology of human memory and learning, and suggest that improving the temporal coordination of neuronal activity in the MTL may provide a novel route to treating memory- and learning-related disorders