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

Interictal Network Dynamics in Paediatric Epilepsy Surgery

Epilepsy is an archetypal brain network disorder. Despite two decades of research elucidating network mechanisms of disease and correlating these with outcomes, the clinical management of children with epilepsy does not readily integrate network concepts. For example, network measures are not used in presurgical evaluation to guide decision making or surgical management plans. The aim of this thesis was to investigate novel network frameworks from the perspective of a clinician, with the explicit aim of finding measures that may be clinically useful and translatable to directly benefit patient care. We examined networks at three different scales, namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and micro (single unit networks) scales, consistently finding network abnormalities in children being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical translation, using frameworks such as IDEAL to robustly assess the impact of these new technologies on management and outcomes. The thesis sets up a platform from which promising computational technology, that utilises brain network analyses, can be readily translated to benefit patient care

Identification of brain epileptiform discharges from electroencephalograms

Brain interictal epileptiform discharges (IEDs), as the fundamental indicators of seizure, are transient events occurring between two or before seizure onsets, captured using electroencephalogram (EEG). For epilepsy diagnosis and localization of seizure sources, both interictal and ictal recordings are extremely informative. Accurate detection of IEDs from over the scalp helps faster diagnosis of epilepsy. The scalp EEG (sEEG) suffers from a low signal-to-noise ratio and high attenuation of IEDs due to the high skull electrical impedance. On the other hand, the intracranial EEG (iEEG) recorded using implanted electrodes enjoys high temporal-spatial resolution and enables capturing most IEDs. Therefore, in this thesis, the focus is on the identification of IEDs from the concurrent scalp and intracranial EEGs. Multi-way analysis provides an opportunity to jointly analyse the data in different domains. IEDs may share some features within and between the segments. We have developed methods based on multi-way analysis and tensor factorization to detect the IEDs from the concurrent sEEG in both segmented and real-time approaches. The diversities in IED morphology, strength, and source location within the brain cause a great deal of uncertainty in their labeling by clinicians. We have exploited and incorporated this uncertainty (the probability of the waveform being an IED) in an IED detection system. Furthermore, IEDs are naturally sparse. We have benefited from the sparsity of IED waveforms in developing an algorithm to exploit sparse common features among the IED segments, referred to as sparse common feature analysis. By mapping sEEG to iEEG, the sEEG quality is improved. In this thesis, the proposed tensor factorization maps the time-frequency features of sEEG to those of iEEG to detect the IEDs from over the scalp with high sensitivity. We have concatenated time, frequency, and channel modes of iEEG recordings into a tensor. After decomposing the tensor into temporal, spectral, and spatial components, the EEG time-frequency features have been extracted and projected onto the temporal components. Furthermore, we have developed two novel algorithms based on generative adversarial networks to map the raw sEEG to iEEG. As a result of this work, the visibility of IEDs from sEEG has over 4-fold improvement. Additionally, the outcome paves the path for future research in epilepsy prediction, seizure source localisation, and modeling the brain seizure pathways

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

The Value of Seizure Semiology in Epilepsy Surgery: Epileptogenic-Zone Localisation in Presurgical Patients using Machine Learning and Semiology Visualisation Tool

Background Eight million individuals have focal drug resistant epilepsy worldwide. If their epileptogenic focus is identified and resected, they may become seizure-free and experience significant improvements in quality of life. However, seizure-freedom occurs in less than half of surgical resections. Seizure semiology - the signs and symptoms during a seizure - along with brain imaging and electroencephalography (EEG) are amongst the mainstays of seizure localisation. Although there have been advances in algorithmic identification of abnormalities on EEG and imaging, semiological analysis has remained more subjective. The primary objective of this research was to investigate the localising value of clinician-identified semiology, and secondarily to improve personalised prognostication for epilepsy surgery. Methods I data mined retrospective hospital records to link semiology to outcomes. I trained machine learning models to predict temporal lobe epilepsy (TLE) and determine the value of semiology compared to a benchmark of hippocampal sclerosis (HS). Due to the hospital dataset being relatively small, we also collected data from a systematic review of the literature to curate an open-access Semio2Brain database. We built the Semiology-to-Brain Visualisation Tool (SVT) on this database and retrospectively validated SVT in two separate groups of randomly selected patients and individuals with frontal lobe epilepsy. Separately, a systematic review of multimodal prognostic features of epilepsy surgery was undertaken. The concept of a semiological connectome was devised and compared to structural connectivity to investigate probabilistic propagation and semiology generation. Results Although a (non-chronological) list of patients’ semiologies did not improve localisation beyond the initial semiology, the list of semiology added value when combined with an imaging feature. The absolute added value of semiology in a support vector classifier in diagnosing TLE, compared to HS, was 25%. Semiology was however unable to predict postsurgical outcomes. To help future prognostic models, a list of essential multimodal prognostic features for epilepsy surgery were extracted from meta-analyses and a structural causal model proposed. Semio2Brain consists of over 13000 semiological datapoints from 4643 patients across 309 studies and uniquely enabled a Bayesian approach to localisation to mitigate TLE publication bias. SVT performed well in a retrospective validation, matching the best expert clinician’s localisation scores and exceeding them for lateralisation, and showed modest value in localisation in individuals with frontal lobe epilepsy (FLE). There was a significant correlation between the number of connecting fibres between brain regions and the seizure semiologies that can arise from these regions. Conclusions Semiology is valuable in localisation, but multimodal concordance is more valuable and highly prognostic. SVT could be suitable for use in multimodal models to predict the seizure focus

Dynamical Modeling Techniques for Biological Time Series Data

The present thesis is articulated over two main topics which have in common the modeling of the dynamical properties of complex biological systems from large-scale time-series data. On one hand, this thesis analyzes the inverse problem of reconstructing Gene Regulatory Networks (GRN) from gene expression data. This first topic seeks to reverse-engineer the transcriptional regulatory mechanisms involved in few biological systems of interest, vital to understand the specificities of their different responses. In the light of recent mathematical developments, a novel, flexible and interpretable modeling strategy is proposed to reconstruct the dynamical dependencies between genes from short-time series data. In addition, experimental trade-offs and optimal modeling strategies are investigated for given data availability. Consistent literature on these topics was previously surprisingly lacking. The proposed methodology is applied to the study of circadian rhythms, which consists in complex GRN driving most of daily biological activity across many species. On the other hand, this manuscript covers the characterization of dynamically differentiable brain states in Zebrafish in the context of epilepsy and epileptogenesis. Zebrafish larvae represent a valuable animal model for the study of epilepsy due to both their genetic and dynamical resemblance with humans. The fundamental premise of this research is the early apparition of subtle functional changes preceding the clinical symptoms of seizures. More generally, this idea, based on bifurcation theory, can be described by a progressive loss of resilience of the brain and ultimately, its transition from a healthy state to another characterizing the disease. First, the morphological signatures of seizures generated by distinct pathological mechanisms are investigated. For this purpose, a range of mathematical biomarkers that characterizes relevant dynamical aspects of the neurophysiological signals are considered. Such mathematical markers are later used to address the subtle manifestations of early epileptogenic activity. Finally, the feasibility of a probabilistic prediction model that indicates the susceptibility of seizure emergence over time is investigated. The existence of alternative stable system states and their sudden and dramatic changes have notably been observed in a wide range of complex systems such as in ecosystems, climate or financial markets