The spatiotemporal dynamics of human focal seizures

Spontaneous human focal seizures can present with a plethora of behavioral manifestations that vary according to the affected cortical regions; however, several key features have been consistently observed. During my doctoral studies, I applied both theoretical and experimental methods to study mechanisms underpinning these consistently seen dynamics. I first analyzed human intracranial EEG recordings, describing statistical methods for measuring their electrophysiological signatures. I next proposed several neurophysiological hypotheses that could explain seizure dynamics and verified them in rodent seizure models. Finally, a computational model was developed, successfully explaining how the complex spatiotemporal evolution of focal seizures emerges from simple neurophysiological principles. In Chapter 1, the long-standing behavioral manifestations and the most up-to-date electrophysiology findings are reviewed. This section details the inspiration for the studies reported in the subsequent chapters. In Chapter 2, I describe several statistical methods for estimating traveling wave velocities. I show most ictal discharges can be described as traveling waves whose velocities contain rich information about the stages of seizure evolution. I compare performance of various statistical methods and propose a robust approach to boost the quality of each method’s estimation results. In Chapter 3, I show how inhibition modulates seizure propagation patterns. Surround inhibition spatially restrains focal seizures and masks excitatory projections of ictal activities. When compromised, two patterns of seizure propagation emerge according to the position of inhibition defects relative to the ictal focus. I show that two distant ictal foci can communicate via physiological connectivity without any chronic rewiring processes – confirming the existence of long-range propagation pathways that could lead to epileptic network formation. In Chapter 4, I show that thalamic inputs might be necessary for interictal epileptiform discharges (IEDs). The relative positions between IEDs and ictal foci indicate that surround inhibition, shown in the previous chapter, can be exhausted by repetitive exposure to ictal projections. In Chapter 5, I propose a neural network model that can explain both long-standing behavioral observations of seizures and account for the most up-to-date electrophysiological recordings of spontaneous human focal seizures. The model relies on few assumptions, all of which are proved or supported in earlier chapters of this thesis. The model explains phasic evolution of seizure dynamics – how the commonly observed patterns arise from simple neurophysiological principles, as well as seizure onset subtypes, traveling wave directions and speeds. The model also predicts how spontaneous seizures might arise from synaptic plasticity. The chapter ends with a discussion of the model’s implications and future work. The thesis is organized in a way that each chapter can be read independently, with Chapter 5 summarizing the central theory spanning the whole study. Each chapter is also tightly linked to a clinically relevant question. In sum, the dissertation’s goal is to provide an in-principle understanding of focal seizure dynamics. With rapid advancement of clinical and experimental tools, I believe this work provides a roadmap for future therapies for epilepsy patients

Study of the Term Neonatal Brain Injury with combined Diffuse Optical Tomography and Electroencephalography

This thesis describes the application of combined diffuse optical tomography (DOT) and electroencephalography (EEG) in the investigation of neonatal term brain injury. With hypoxic ischaemic encephalopathy (HIE) and perinatal stroke being the most frequent contributors to brain injury in the term neonatal population, the first part of the thesis focuses on the description and ongoing requirement for their further investigation. In continuation to that, the characteristics and unique properties of both DOT and EEG are described and explored. The combination of these two modalities was utilised in elucidating the relationship between neuronal activity and cerebral haemodynamics both in physiological processes as well as in disease, by the infant’s cot side. This work differs to previous studies using near-infrared technologies and EEG, as a denser whole head array was used, offering the potential of 3-dimensional image reconstruction of the cortical haemodynamic events in relation to electro-cortical activity. These methods were applied in the study of critically ill infants presenting with seizures in the first few days of life. The relevant results are presented in three separate chapters of the thesis. Distinct neurophysiological phenomena such as seizures and burst suppression were detected and studied in association to underlying HIE. On the grounds of a pre-existing pilot study of our research group, distinct prolonged de-oxygenated cortical areas were identified following electrical seizure activity. Further exploration of infants with seizures provided limited supporting evidence. The investigation of burst suppression in HIE led to the first ever identification of repeated, waveform, cortical haemodynamic events in response to bursts of electrical activity with some spatial correlation to regions of brain injury. Further analysis of the low frequencies within the diffuse optical signal in cases of perinatal stroke, showed a consistent interhemispheric difference between the healthy and stroke-affected brain regions. The limitations, prospects and conclusions are presented in the final chapter. The use of simultaneous DOT and EEG offers a unique neuro-monitoring and neuro-investigating tool in the neonatal intensive care environment, which is safe, portable, and cost-effective, Ongoing research is required for the exploration and development of the methodology and its potential diagnostic properties

Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution

Epileptic seizures are characterised by abnormal brain dynamics at multiple scales, engaging single neurons, neuronal ensembles and coarse brain regions. Key to understanding the cause of such emergent population dynamics, is capturing the collective behaviour of neuronal activity at multiple brain scales. In this thesis I make use of the larval zebrafish to capture single cell neuronal activity across the whole brain during epileptic seizures. Firstly, I make use of statistical physics methods to quantify the collective behaviour of single neuron dynamics during epileptic seizures. Here, I demonstrate a population mechanism through which single neuron dynamics organise into seizures: brain dynamics deviate from a phase transition. Secondly, I make use of single neuron network models to identify the synaptic mechanisms that actually cause this shift to occur. Here, I show that the density of neuronal connections in the network is key for driving generalised seizure dynamics. Interestingly, such changes also disrupt network response properties and flexible dynamics in brain networks, thus linking microscale neuronal changes with emergent brain dysfunction during seizures. Thirdly, I make use of non-linear causal inference methods to study the nature of the underlying neuronal interactions that enable seizures to occur. Here I show that seizures are driven by high synchrony but also by highly non-linear interactions between neurons. Interestingly, these non-linear signatures are filtered out at the macroscale, and therefore may represent a neuronal signature that could be used for microscale interventional strategies. This thesis demonstrates the utility of studying multi-scale dynamics in the larval zebrafish, to link neuronal activity at the microscale with emergent properties during seizures

Deep learning approach for epileptic seizure detection

Abstract. Epilepsy is the most common brain disorder that affects approximately fifty million people worldwide, according to the World Health Organization. The diagnosis of epilepsy relies on manual inspection of EEG, which is error-prone and time-consuming. Automated epileptic seizure detection of EEG signal can reduce the diagnosis time and facilitate targeting of treatment for patients. Current detection approaches mainly rely on the features that are designed manually by domain experts. The features are inflexible for the detection of a variety of complex patterns in a large amount of EEG data. Moreover, the EEG is non-stationary signal and seizure patterns vary across patients and recording sessions. EEG data always contain numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges deep learning approaches are examined in this paper. Deep learning methods were applied to a large publicly available dataset, the Children’s Hospital of Boston-Massachusetts Institute of Technology dataset (CHB-MIT). The present study includes three experimental groups that are grouped based on the pre-processing steps. The experimental groups contain 3–4 experiments that differ between their objectives. The time-series EEG data is first pre-processed by certain filters and normalization techniques, and then the pre-processed signal was segmented into a sequence of non-overlapping epochs. Second, time series data were transformed into different representations of input signals. In this study time-series EEG signal, magnitude spectrograms, 1D-FFT, 2D-FFT, 2D-FFT magnitude spectrum and 2D-FFT phase spectrum were investigated and compared with each other. Third, time-domain or frequency-domain signals were used separately as a representation of input data of VGG or DenseNet 1D. The best result was achieved with magnitude spectrograms used as representation of input data in VGG model: accuracy of 0.98, sensitivity of 0.71 and specificity of 0.998 with subject dependent data. VGG along with magnitude spectrograms produced promising results for building personalized epileptic seizure detector. There was not enough data for VGG and DenseNet 1D to build subject-dependent classifier.Epileptisten kohtausten havaitseminen syväoppimisella lähestymistavalla. Tiivistelmä. Epilepsia on yleisin aivosairaus, joka Maailman terveysjärjestön mukaan vaikuttaa noin viiteenkymmeneen miljoonaan ihmiseen maailmanlaajuisesti. Epilepsian diagnosointi perustuu EEG:n manuaaliseen tarkastamiseen, mikä on virhealtista ja aikaa vievää. Automaattinen epileptisten kohtausten havaitseminen EEG-signaalista voi potentiaalisesti vähentää diagnoosiaikaa ja helpottaa potilaan hoidon kohdentamista. Nykyiset tunnistusmenetelmät tukeutuvat pääasiassa piirteisiin, jotka asiantuntijat ovat määritelleet manuaalisesti, mutta ne ovat joustamattomia monimutkaisten ilmiöiden havaitsemiseksi suuresta määrästä EEG-dataa. Lisäksi, EEG on epästationäärinen signaali ja kohtauspiirteet vaihtelevat potilaiden ja tallennusten välillä ja EEG-data sisältää aina useita kohinatyyppejä, jotka huonontavat epilepsiakohtauksen havaitsemisen tarkkuutta. Näihin haasteisiin vastaamiseksi tässä diplomityössä tarkastellaan soveltuvatko syväoppivat menetelmät epilepsian havaitsemiseen EEG-tallenteista. Aineistona käytettiin suurta julkisesti saatavilla olevaa Bostonin Massachusetts Institute of Technology lastenklinikan tietoaineistoa (CHB-MIT). Tämän työn tutkimus sisältää kolme koeryhmää, jotka eroavat toisistaan esikäsittelyvaiheiden osalta: aikasarja-EEG-data esikäsiteltiin perinteisten suodattimien ja normalisointitekniikoiden avulla, ja näin esikäsitelty signaali segmentoitiin epookkeihin. Kukin koeryhmä sisältää 3–4 koetta, jotka eroavat menetelmiltään ja tavoitteiltaan. Kussakin niistä epookkeihin jaettu aikasarjadata muutettiin syötesignaalien erilaisiksi esitysmuodoiksi. Tässä tutkimuksessa tutkittiin ja verrattiin keskenään EEG-signaalia sellaisenaan, EEG-signaalin amplitudi-spektrogrammeja, 1D-FFT-, 2D-FFT-, 2D-FFT-amplitudi- ja 2D-FFT -vaihespektriä. Näin saatuja aika- ja taajuusalueen signaaleja käytettiin erikseen VGG- tai DenseNet 1D -mallien syötetietoina. Paras tulos saatiin VGG-mallilla kun syötetietona oli amplitudi-spektrogrammi ja tällöin tarkkuus oli 0,98, herkkyys 0,71 ja spesifisyys 0,99 henkilöstä riippuvaisella EEG-datalla. VGG yhdessä amplitudi-spektrogrammien kanssa tuottivat lupaavia tuloksia henkilökohtaisen epilepsiakohtausdetektorin rakentamiselle. VGG- ja DenseNet 1D -malleille ei ollut tarpeeksi EEG-dataa henkilöstä riippumattoman luokittelijan opettamiseksi

Dynamic mechanisms of neocortical focal seizure onset.

Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tRecent experimental and clinical studies have provided diverse insight into the mechanisms of human focal seizure initiation and propagation. Often these findings exist at different scales of observation, and are not reconciled into a common understanding. Here we develop a new, multiscale mathematical model of cortical electric activity with realistic mesoscopic connectivity. Relating the model dynamics to experimental and clinical findings leads us to propose three classes of dynamical mechanisms for the onset of focal seizures in a unified framework. These three classes are: (i) globally induced focal seizures; (ii) globally supported focal seizures; (iii) locally induced focal seizures. Using model simulations we illustrate these onset mechanisms and show how the three classes can be distinguished. Specifically, we find that although all focal seizures typically appear to arise from localised tissue, the mechanisms of onset could be due to either localised processes or processes on a larger spatial scale. We conclude that although focal seizures might have different patient-specific aetiologies and electrographic signatures, our model suggests that dynamically they can still be classified in a clinically useful way. Additionally, this novel classification according to the dynamical mechanisms is able to resolve some of the previously conflicting experimental and clinical findings.This work was supported by the Doctoral Training Centre in Systems Biology (University of Manchester), the Biotechnology and Biological Sciences Research Council, and the Engineering and Physical Sciences Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Apport de nouvelles techniques dans l’évaluation de patients candidats à une chirurgie d’épilepsie : résonance magnétique à haut champ, spectroscopie proche infrarouge et magnétoencéphalographie

L'épilepsie constitue le désordre neurologique le plus fréquent après les maladies cérébrovasculaires. Bien que le contrôle des crises se fasse généralement au moyen d'anticonvulsivants, environ 30 % des patients y sont réfractaires. Pour ceux-ci, la chirurgie de l'épilepsie s'avère une option intéressante, surtout si l’imagerie par résonance magnétique (IRM) cérébrale révèle une lésion épileptogène bien délimitée. Malheureusement, près du quart des épilepsies partielles réfractaires sont dites « non lésionnelles ». Chez ces patients avec une IRM négative, la délimitation de la zone épileptogène doit alors reposer sur la mise en commun des données cliniques, électrophysiologiques (EEG de surface ou intracrânien) et fonctionnelles (tomographie à émission monophotonique ou de positrons). La faible résolution spatiale et/ou temporelle de ces outils de localisation se traduit par un taux de succès chirurgical décevant. Dans le cadre de cette thèse, nous avons exploré le potentiel de trois nouvelles techniques pouvant améliorer la localisation du foyer épileptique chez les patients avec épilepsie focale réfractaire considérés candidats potentiels à une chirurgie d’épilepsie : l’IRM à haut champ, la spectroscopie proche infrarouge (SPIR) et la magnétoencéphalographie (MEG). Dans une première étude, nous avons évalué si l’IRM de haut champ à 3 Tesla (T), présentant théoriquement un rapport signal sur bruit plus élevé que l’IRM conventionnelle à 1,5 T, pouvait permettre la détection des lésions épileptogènes subtiles qui auraient été manquées par cette dernière. Malheureusement, l’IRM 3 T n’a permis de détecter qu’un faible nombre de lésions épileptogènes supplémentaires (5,6 %) d’où la nécessité d’explorer d’autres techniques. Dans les seconde et troisième études, nous avons examiné le potentiel de la SPIR pour localiser le foyer épileptique en analysant le comportement hémodynamique au cours de crises temporales et frontales. Ces études ont montré que les crises sont associées à une augmentation significative de l’hémoglobine oxygénée (HbO) et l’hémoglobine totale au niveau de la région épileptique. Bien qu’une activation contralatérale en image miroir puisse être observée sur la majorité des crises, la latéralisation du foyer était possible dans la plupart des cas. Une augmentation surprenante de l’hémoglobine désoxygénée a parfois pu être observée suggérant qu’une hypoxie puisse survenir même lors de courtes crises focales. Dans la quatrième et dernière étude, nous avons évalué l’apport de la MEG dans l’évaluation des patients avec épilepsie focale réfractaire considérés candidats potentiels à une chirurgie. Il s’est avéré que les localisations de sources des pointes épileptiques interictales par la MEG ont eu un impact majeur sur le plan de traitement chez plus des deux tiers des sujets ainsi que sur le devenir postchirurgical au niveau du contrôle des crises.Epilepsy is the most common chronic neurological disorder after stroke. The major form of treatment is long-term drug therapy to which approximately 30% of patients are unfortunately refractory to. Brain surgery is recommended when medication fails, especially if magnetic resonance imaging (MRI) can identify a well-defined epileptogenic lesion. Unfortunately, close to a quarter of patients have nonlesional refractory focal epilepsy. For these MRI-negative cases, identification of the epileptogenic zone rely heavily on remaining tools: clinical history, video-electroencephalography (EEG) monitoring, ictal single-photon emission computed tomography (SPECT), and a positron emission tomography (PET). Unfortunately, the limited spatial and/or temporal resolution of these localization techniques translates into poor surgical outcome rates. In this thesis, we explore three relatively novel techniques to improve the localization of the epileptic focus for patients with drug-resistant focal epilepsy who are potential candidates for epilepsy surgery: high-field 3 Tesla (T) MRI, near-infrared spectroscopy (NIRS) and magnetoencephalography (MEG). In the first study, we evaluated if high-field 3T MRI, providing a higher signal to noise ratio, could help detect subtle epileptogenic lesions missed by conventional 1.5T MRIs. Unfortunately, we show that the former was able to detect an epileptogenic lesion in only 5.6% of cases of 1.5T MRI-negative epileptic patients, emphasizing the need for additional techniques. In the second and third studies, we evaluated the potential of NIRS in localizing the epileptic focus by analyzing the hemodynamic behavior of temporal and frontal lobe seizures respectively. We show that focal seizures are associated with significant increases in oxygenated haemoglobin (HbO) and total haemoglobin (HbT) over the epileptic area. While a contralateral mirror-like activation was seen in the majority of seizures, lateralization of the epileptic focus was possible most of the time. In addition, an unexpected increase in deoxygenated haemoglobin (HbR) was noted in some seizures, suggesting possible hypoxia even during relatively brief focal seizures. In the fourth and last study, the utility of MEG in the evaluation of nonlesional drug-refractory focal epileptic patients was studied. It was found that MEG source localization of interictal epileptic spikes had an impact both on patient management for over two thirds of patients and their surgical outcome