Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

We apply flicker-noise spectroscopy (FNS), a time series analysis method operating on structure functions and power spectrum estimates, to study the clinical electroencephalogram (EEG) signals recorded in children/adolescents (11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical Sciences. The EEG signals for these subjects were compared with the signals for a control sample of chronically depressed children/adolescents. The purpose of the study is to look for diagnostic signs of subjects' susceptibility to schizophrenia in the FNS parameters for specific electrodes and cross-correlations between the signals simultaneously measured at different points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes at locations F3 and F4, which are symmetrically positioned in the left and right frontal areas of cerebral cortex, respectively, demonstrates an essential role of frequency-phase synchronization, a phenomenon representing specific correlations between the characteristic frequencies and phases of excitations in the brain. We introduce quantitative measures of frequency-phase synchronization and systematize the values of FNS parameters for the EEG data. The comparison of our results with the medical diagnoses for 84 subjects performed at NCPH makes it possible to group the EEG signals into 4 categories corresponding to different risk levels of subjects' susceptibility to schizophrenia. We suggest that the introduced quantitative characteristics and classification of cross-correlations may be used for the diagnosis of schizophrenia at the early stages of its development.Comment: 36 pages, 6 figures, 2 tables; to be published in "Physica A

A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

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

Dynamic Complexity and Causality Analysis of Scalp EEG for Detection of Cognitive Deficits

This dissertation explores the potential of scalp electroencephalography (EEG) for the detection and evaluation of neurological deficits due to moderate/severe traumatic brain injury (TBI), mild cognitive impairment (MCI), and early Alzheimer’s disease (AD). Neurological disorders often cannot be accurately diagnosed without the use of advanced imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Non-quantitative task-based examinations are also used. None of these techniques, however, are typically performed in the primary care setting. Furthermore, the time and expense involved often deters physicians from performing them, leading to potential worse prognoses for patients. If feasible, screening for cognitive deficits using scalp EEG would provide a fast, inexpensive, and less invasive alternative for evaluation of TBI post injury and detection of MCI and early AD. In this work various measures of EEG complexity and causality are explored as means of detecting cognitive deficits. Complexity measures include eventrelated Tsallis entropy, multiscale entropy, inter-regional transfer entropy delays, and regional variation in common spectral features, and graphical analysis of EEG inter-channel coherence. Causality analysis based on nonlinear state space reconstruction is explored in case studies of intensive care unit (ICU) signal reconstruction and detection of cognitive deficits via EEG reconstruction models. Significant contributions in this work include: (1) innovative entropy-based methods for analyzing event-related EEG data; (2) recommendations regarding differences in MCI/AD of common spectral and complexity features for different scalp regions and protocol conditions; (3) development of novel artificial neural network techniques for multivariate signal reconstruction; and (4) novel EEG biomarkers for detection of dementia

Redes cerebrales en quejas subjetivas de memoria y deterioro cognitivo leve: caracterización de las etapas de pre-demencia mediante magnetoencefalografía

Tesis de la Universidad Complutense de Madrid, Facultad de Psicología, leída el 22/03/2018. Tesis formato europeo (compendio de artículos)La demencia es un cuadro que puede ser originado por múltiples causas, produciendo un deterioro cognitivo muy marcado y limitando la independencia del paciente. La causa más común de demencia es la Enfermedad de Alzheimer (EA) que representa aproximadamente el 60% de los casos totales. Aunque existen numerosos factores que parecen modular el riesgo de desarrollar EA tales como factores genéticos (APOE, PS1, etc.) o variables relacionadas con el estilo de vida (estudios, ocupación, dieta, etc.), la edad es sin duda la variable más influyente y el mayor factor de riesgo ante la aparición de la EA. Por este motivo, el número de personas mayores afectadas por esta enfermedad no ha parado de aumentar durante las últimas décadas, y se espera que aumente su incidencia aún más. Debido al fracaso generalizado de los ensayos farmacológicos, numerosos esfuerzos en investigación se centran ahora en la detección temprana de la EA. El curso de la EA es lento e insidioso, y la acumulación de neuropatología puede comenzar hasta 15 años antes de su diagnóstico. A lo largo de esta etapa preclínica los pacientes atraviesan un estadio conocido como deterioro cognitivo leve (DCL). Esta etapa se caracteriza por alteraciones en uno o varios dominios cognitivos que no genera aún graves alteraciones del funcionamiento diario. Este estadio está altamente asociado al desarrollo posterior de EA y por tanto se considera bajo determinadas condiciones una etapa prodrómica de la enfermedad. Las personas mayores con DCL suelen presentar alteraciones a nivel cerebral o metabólico característicos de la EA, tales como atrofia cortical, alteraciones sinápticas o acumulación de proteínas relacionadas con la fisiopatología de la EA. La literatura científica reciente ha descrito una etapa anterior incluso al DCL que podría asociarse al desarrollo de demencia futuro. Las quejas subjetivas de memoria (QSM) se caracterizarían por la presencia de un sentimiento subjetivo de deterioro cognitivo en ausencia de afectación objetiva, es decir, la evaluación neuropsicológica de estas personas mayores se encuentra en el rango normal. Sin embargo, el estado de la actividad cerebral en esta etapa, o su integridad estructural aún no ha sido apenas descrito. Existen resultados contradictorios con respecto a si la presencia de QSM en personas mayores se asocia a un riesgo más elevado de desarrollar demencia. Además, mientras algunos estudios reportan alteraciones a nivel cerebral compatibles con EA en esta etapa, otros no encuentran tales signos. El objetivo fundamental de esta tesis es la caracterización de las alteraciones en las redes cerebrales en personas mayores sanas, personas mayores con QSM y personas mayores con DCL. El estado actual de la literatura nos permite anticipar la presencia de alteraciones cerebrales relacionadas con EA en el grupo con DCL, sin embargo este trabajo pretende estudiar si dichas alteraciones, o formas más sutiles, se encuentran presentes en el grupo con QSM. Esto nos permitirá en primer lugar clarificar si las QSM tienen alguna relevancia clínica y si se encuentran asociadas a cambios objetivos en la actividad cerebral. Además, se podrá describir el curso exacto de las alteraciones que tienen lugar a lo largo de las etapas preclínicas en la EA gracias a la inclusión del grupo con DCL, caracterizando así en cada estudio las dos etapas que anteceden a la EA descritas a día de hoy...Dementia is a clinical entity producing major cognitive impairment that interferes with daily living activities that can be caused by a variety of conditions. Among them, Alzheimer´s Disease (AD) represents around a 60% of the total dementia cases. AD risk is modulated by multiple variables such as genotype (APOE, PS1, etc.) or lifestyle variables (studies, occupation, dietary patterns, etc.), although age is the most crucial risk factor for AD development. As a consequence, the number of AD patients has rapidly grown over the last few decades and is expected to increase even more dramatically in the near future. Given the poor results obtained in pharmacological trials to cure or slow AD progression, early AD detection is receiving increasing research efforts over the last few years. Considering the slow and insidious progression of AD, brain pathology starts accumulating in the brain as soon as 15 years before clinical symptoms are severe enough to establish an AD diagnostic. Before reaching AD dementia, patients develop mild cognitive impairment (MCI). This stage is characterized by the presence of a significant cognitive impairment affecting one or more domains. However, this cognitive decline does not significantly limit patients’ daily functioning. MCI patients are known to show increased conversion rates to AD with respect to healthy elders and thus this stage is commonly accepted as a prodromal stage of AD according to recent MCI criteria. MCI patients are known to exhibit AD-like brain and metabolic alterations such as cortical atrophy or AD-related protein accumulation. Recent scientific literature has described a stage preceding MCI which could be associated with future dementia development. Subjective cognitive decline is defined by the presence of a subjective feeling of cognitive worsening in the absence of objective impairment in classical neuropsychological assessment. However, the integrity of brain activity or structure has been scarcely described yet. Furthermore, there exist some contradictory results regarding whether the presence of cognitive concerns is truly related to increased dementia risk. In the same vein, some studies have found brain alterations in SCD patients resembling of those associated with AD while others failed to find such signs. The main objective of this thesis is characterizing brain network alterations in healthy elders, elders with SCD and elders with MCI. The current state-of-the-art lets us anticipate the presence of brain disruption in the MCI group, nonetheless, this work aims to provide evidence of whether similar alterations are already present in the SCD stage. The results presented in this thesis will clarify the clinical relevance of SCD by discerning whether cognitive concerns are truly mediated by network disruption or not. Moreover, the exact course and development of electrophysiological brain alterations during the preclinical stages of the disease will be described by including also MCI patients. By including these three groups we will be able to characterize brain function in the different AD preclinical stages considered in current literature...Fac. de PsicologíaTRUEunpu

Advances in Clinical Neurophysiology

Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests

Laskennalliset muuttujat vastasyntyneen aivomonitoroinnin arvioinnissa

The aim of this thesis was to find out if computational electroencephalography (EEG) features can be used in the automated monitoring of newborns after asphyxia. EEG is already widely used in the neonatal intensive care units but there is a need for quantitative measures that can be obtained without the presence of a clinical expert. One of the biggest challenges in the treatment of newborns with asphyxia is also to correctly estimate the severity of the resulting neurological problems. Eight different feature classes were computed for 42 full-term babies from periods of quiet and active sleep. These feature classes measured correlations of amplitude and phase, interhemispheric synchrony, multifractality and spectral properties. We then studied the ability of these features to distinguish between different severity groups and also tested a classification algorithm to predict the outcome of the babies. Quiet sleep was noted to be more sensitive when separating groups with different grades of severity and most of the used feature classes showed significant results in statistical testing between the groups. The babies with the normal outcome were classified more accurately with the EEG based classification algorithm, than with only the clinical estimation.Työn tarkoituksena oli selvittää, onko aivosähkökäyrästä (EEG) laskettuja parametreja mahdollista käyttää happivajeesta kärsineiden vastasyntyneiden automaattisessa monitoroinnissa. EEG on jo nyt yleisesti käytössä vastasyntyneiden teho-osastoilla, mutta tarve kvantitatiivisille mittareille, joiden tulkintaan ei tarvita lääketieteen asiantuntijaa, on suuri. Lisäksi yksi suurimmista haasteista on pystyä arvioimaan tarkasti, kuinka vakaviin neurologisiin ongelmiin happivaje johtaa. Työssä laskettiin kahdeksan erilaista muuttujajoukkoa 42 täysiaikaiselle vauvalle sekä hiljaisen että aktiivisen unen aikana. Nämä muuttujat mittasivat amplitudin ja vaiheen korrelaatioita, aivopuoliskojen välistä synkroniaa, multifraktaalisuutta sekä taajuusjakaumaa. Tämän jälkeen tutkittiin muuttujien kykyä erotella eri vakavuusasteisia ryhmiä ja testattiin luokittelualgoritmia vauvojen tulevan terveydentilan ennustamiseen. Hiljaisen unen huomattiin olevan herkempi havaitsemaan eroja eri vakavuusasteisten ryhmien välillä ja tilastollisen testauksen perusteella suurin osa valituista muuttujajoukoista erotteli merkittävästi eri vakavuusryhmiä. Ne vauvat, jotka toipuivat hapenpuutteesta täysin, pystyttiin löytämään EEG-pohjaisella luokittimella tarkemmin kuin pelkän kliinisen arvion avulla