Innovative Processing Algorithms for Fetal Magnetoencephalographic Data

Fetale Magnetenzepahalographie (fMEG) ermöglicht die Untersuchung der Entwicklung des zentralen und des autonomen Nervensystems bei Feten ab der 20. Schwangerschaftswoche. Wie normale Magnetenzephalographie bei Erwachsenen und Kindern ist auch fMEG eine nicht-invasive Methode und in der Anwendung vollkommen harmlos für Mutter und Kind. Die magnetischen Sensoren sind hierbei um das Abdomen der schwangeren Frau angeordnet. Die gute räumliche und zeitliche Auflösung erlaubt es, mütterliche und fetale Magnetokardiogramme gleichzeitig mit der fetalen Hirnaktivität zu messen. Die Signale der fetalen Magnetoenzephalographie werden vor allem zur Messung von auditiven und visuellen ereignisbezogenen Hirnreaktionen oder der spontanen Hirnaktivität verwendet. Wichtige Fragen zum Entwicklungsprozess des fetalen Gehirns und des autonomen Nervensystems sowie der mütterliche Einfluss auf den metabolischen und kognitiven Zustand des Neugeborenen können durch die Analyse der fetalen Magnetoenzephalographie-Signale geklärt werden. Die Auswertung der fetalen Hirnaktivität birgt einige Herausforderungen, da die Signale der fetalen und mütterlichen Herzaktivität etwa 10-1000 mal stärker sind als das fetale Hirnsignal. Daher ist es zwingend erforderlich, die Herzaktivität der Mutter und des Fetus zu erkennen und zu entfernen, bevor die fetale Hirnaktivität analysiert wird. Die derzeit verwendeten Methoden für die Erkennung und Entfernung der Herzaktivität funktionieren für die meisten Datensätze zuverlässig, die Verarbeitung enthält jedoch einige manuelle Schritte, was das Ganze sehr zeitaufwändig macht. Darüber hinaus ist die Signal Redistribution beim Entfernen der Herzaktivität ein bekanntes Problem, welches es schwierig macht, die Hirnaktivität später zu identifizieren. Das Ziel dieser Arbeit war es, die Auswertung der fMEG Daten schneller, besser und trotzdem leicht handhabbar zu machen. In dieser Arbeit werden zwei neue vollautomatisierte Methoden zur Erkennung und Entfernung der Herzaktivität vorgestellt. Der vollautomatisierte R-Peak Erkennungsalgorithmus (FLORA) verbessert die R-Peak Erkennung, indem er die Vorteile der zuvor verwendeten Methoden kombiniert und erweitert. Der Algorithmus zur vollautomatisierten Subtraktion der Herzaktivität (FAUNA) verbessert die Signalqualität und vereinfacht die Erkennung der Hirnaktivität, ohne Redistribution. Die Zuverlässigkeit der Daten wird dadurch erhöht, da keine manuelle Auswahl getroffen werden muss. Die Kombination beider Methoden in einem Programm zur vollautomatisierten Verarbeitung für die fetale Magnetoenzephalographie (FAIRY) macht die Datenauswertung nun einfach und schnell. Damit wird die fMEG Datenverarbeitung auf die "Big Data"- und "Automated Science"-Ära vorbereitet. Des Weiteren wurde eine Studie über die autonome und zentralnervöse Reaktion von Feten und Neugeborenen auf die mütterliche Stimme (AURORA) mit den neuen Datenverarbeitungsmethoden durchgeführt. Die Ergebnisse zeigten eine reduzierte Bewegung der Feten zwischen der 26. und 32. Schwangerschaftswoche und eine niedrigere Herzfrequenz während der ersten 20 Sekunden der Stimulation in den letzten Schwangerschaftswochen, als Reaktion auf die mütterliche Stimme. Zusätzlich fanden wir eine höhere Amplitude der Gehirnreaktion als Reaktion auf eine fremde Frauenstimme bei Neugeborenen.Fetal magnetoencephalography (fMEG) facilitates the investigation of both the nature and development of the fetal central and autonomic nervous system, starting at 20 weeks of gestational age. Like magnetoencephalography in children and adults, fetal magnetoencephalography is a noninvasive method and therefore completely harmless for both the mother and the child. Magnetic sensors in fMEG devices are arranged around the abdomen of the pregnant woman. The good spatial and temporal resolution allows to measure maternal and fetal magnetocardiograms simultaneously with fetal brain activity. The fMEG signals are mainly used to measure the auditory and visual event-related brain responses or the spontaneous brain activity. Important questions concerning the developmental process of the fetal brain, as well as the maternal influence on the metabolic and cognitive state of the newborn, can be clarified by the analysis of fMEG signals. The evaluation of the fetal brain activity poses some challenges, as the signals of fetal and maternal heart activity are 10-1000 times stronger than the fetal brain signal. Therefore, it is mandatory to detect and remove the heart activity of both the mother and the fetus before analyzing the fetal brain activity. The currently used methods for this detection and removal work well for most datasets, but the processing includes numerous manual steps and is therefore very time consuming. Furthermore, signal redistribution is a problem with the current methods, which makes later detection of the fetal brain activity challenging. The aim of this work was to make the evaluation of fMEG data faster, better and nevertheless, easy to use. In this thesis two new fully-automated procedures for the detection and removal of the heart activity are presented. The fully automated R-peak detection algorithm (FLORA) improves R-peak detection by combining and extending the advantages of the previously used methods. The algorithm for the fully automated subtraction of heart activity (FAUNA) improves the signal quality and facilitates detection of brain activity without the problem of redistribution. Furthermore these methods lead to a higher reliability of the data analysis since no manual interventions are necessary. Combining both methods in a tool for fully automated processing for fetal magnetoencephalography (FAIRY) makes data evaluation now easy and fast. This prepares the processing of fMEG data for the era of "Big Data" and "Automated Science". Additionally a study about the fetal and neonatal autonomous and central nervous response to maternal voice (AURORA) was performed using the new data processing methods. The results showed a reduced movement of fetuses between 26 and 32 weeks of pregnancy and a lower heart rate during the fist 20 seconds of stimulation in the last weeks of pregnancy as a reaction to maternal voice. We additionally found a higher amplitude of the brain response to voice onset of a stranger female voice in newborns

Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data.

BACKGROUND AND OBJECTIVE: Fetal magnetoencephalography (fMEG) is a method for recording fetal brain signals, fetal and maternal heart activity simultaneously. The identification of the R-peaks of the heartbeats forms the basis for later heart rate (HR) and heart rate variability (HRV) analysis. The current procedure for the evaluation of fetal magnetocardiograms (fMCG) is either semi-automated evaluation using template matching (SATM) or Hilbert transformation algorithm (HTA). However, none of the methods available at present works reliable for all datasets. METHODS: Our aim was to develop a unitary, responsive and fully automated R-peak detection algorithm (FLORA) that combines and enhances both of the methods used up to now. RESULTS: The evaluation of all methods on 55 datasets verifies that FLORA outperforms both of these methods as well as a combination of the two, which applies in particular to data of fetuses at earlier gestational age. CONCLUSION: The combined analysis shows that FLORA is capable of providing good, stable and reproducible results without manual intervention

New Insights into Molecular Mechanisms Underlying Neurodegenerative Disorders

Neurodegenerative disorders encompass a broad range of sporadic and/or familial debilitating conditions characterized by the progressive dysfunction and loss of selective neuronal populations, determining different clinical phenotypes. Emerging research data indicate an interplay of genetic factors and epigenetic mechanisms underlying neurodegenerative processes, which lead to increased prevalence of neurodegenerative disorders. In concert with the constant increase in the aging population, neurodegenerative disorders currently represent a major challenge to public health worldwide. Despite recent advances in clinical and preclinical research, the pathogenesis of these disorders still remains poorly understood, without effective treatments being available to halt the neurodegenerative processes, but rather aiming at relieving symptoms. Therefore, a critical evaluation of current research data and in-depth understanding of the molecular mechanisms that lead to neurodegeneration are crucial in order to identify potential therapeutic targets that can pave the way to the development of novel and promising therapies. This Special Issue is focused on novel molecular data in the field of neurodegeneration that associate with the onset and progression of neurodegenerative diseases. We are particularly interested in original articles and reviews that provide new insights into the main molecular pathogenic mechanisms underlying neurodegenerative disorders, aiming to identify potential biomarkers and novel therapeutic strategies

Medical-Data-Models.org:A collection of freely available forms (September 2016)

MDM-Portal (Medical Data-Models) is a meta-data repository for creating, analysing, sharing and reusing medical forms, developed by the Institute of Medical Informatics, University of Muenster in Germany. Electronic forms for documentation of patient data are an integral part within the workflow of physicians. A huge amount of data is collected either through routine documentation forms (EHRs) for electronic health records or as case report forms (CRFs) for clinical trials. This raises major scientific challenges for health care, since different health information systems are not necessarily compatible with each other and thus information exchange of structured data is hampered. Software vendors provide a variety of individual documentation forms according to their standard contracts, which function as isolated applications. Furthermore, free availability of those forms is rarely the case. Currently less than 5 % of medical forms are freely accessible. Based on this lack of transparency harmonization of data models in health care is extremely cumbersome, thus work and know-how of completed clinical trials and routine documentation in hospitals are hard to be re-used. The MDM-Portal serves as an infrastructure for academic (non-commercial) medical research to contribute a solution to this problem. It already contains more than 4,000 system-independent forms (CDISC ODM Format, www.cdisc.org, Operational Data Model) with more than 380,000 dataelements. This enables researchers to view, discuss, download and export forms in most common technical formats such as PDF, CSV, Excel, SQL, SPSS, R, etc. A growing user community will lead to a growing database of medical forms. In this matter, we would like to encourage all medical researchers to register and add forms and discuss existing forms