1,329 research outputs found
The Role of Europe in World-Wide Science and Technology: Monitoring and Evaluation in a Context of Global Competition
Noyons ECM, Buter RK, van Raan AFJ, Schwechheimer H, Winterhager M, Weingart P. The Role of Europe in World-Wide Science and Technology: Monitoring and Evaluation in a Context of Global Competition. Leiden: Universiteit Leiden; 2000
EEG-Biofeedback and epilepsy: concept, methodology and tools for (neuro)therapy planning and objective evaluation
EEG-Biofeedback and Epilepsy: Concept, Methodology and Tools for
(Neuro)therapy Planning and Objective Evaluation
ABSTRACT
Objective diagnosis and therapy evaluation are still challenging tasks for many neurological
disorders. This is highly related to the diversity of cases and the variety of treatment modalities
available. Especially in the case of epilepsy, which is a complex disorder not well-explained at the
biochemical and physiological levels, there is the need for investigations for novel features, which
can be extracted and quantified from electrophysiological signals in clinical practice. Neurotherapy
is a complementary treatment applied in various disorders of the central nervous system, including
epilepsy. The method is subsumed under behavioral medicine and is considered an operant
conditioning in psychological terms. Although the application areas of this promising
unconventional approach are rapidly increasing, the method is strongly debated, since the
neurophysiological underpinnings of the process are not yet well understood. Therefore, verification
of the efficacy of the treatment is one of the core issues in this field of research.
Considering the diversity in epilepsy and its various treatment modalities, a concept and a
methodology were developed in this work for increasing objectivity in diagnosis and therapy
evaluation. The approach can also fulfill the requirement of patient-specific neurotherapy planning.
Neuroprofile is introduced as a tool for defining a structured set of quantifiable measures which can
be extracted from electrophysiological signals. A set of novel quantitative features (i.e., percentage
epileptic pattern occurrence, contingent negative variation level difference measure, direct current
recovery index, heart rate recovery ratio, and hyperventilation heart rate index) were defined, and
the methods were introduced for extracting them. A software concept and the corresponding tools
(i.e., the neuroprofile extraction module and a database) were developed as a basis for automation to
support the methodology.
The features introduced were investigated through real data, which were acquired both in laboratory
studies with voluntary control subjects and in clinical applications with epilepsy patients. The results
indicate the usefulness of the introduced measures and possible benefits of integrating the indices
obtained from electroencephalogram (EEG) and electrocardiogram for diagnosis and therapy
evaluation. The applicability of the methodology was demonstrated on sample cases for therapy
evaluation. Based on the insights gained through the work, synergetics was proposed as a theoretical
framework for comprehending neurotherapy as a complex process of learning. Furthermore, direct
current (DC)-level in EEG was hypothesized to be an order parameter of the brain complex open
system. For future research in this field, investigation of the interactions between higher cognitive
functions and the autonomous nervous system was proposed.
Keywords: EEG-biofeedback, epilepsy, neurotherapy, slow cortical potentials, objective diagnosis,
therapy evaluation, epileptic pattern quantification, fractal dimension, contingent negative variation,
hyperventilation, DC-shifts, instantaneous heart rate, neuroprofile, database system, synergetics.Die Epilepsie ist eine komplexe neurologische Erkrankung, die auf biochemischer und physiologischer Ebene nicht ausreichend geklärt ist. Die Vielfalt der epileptischen Krankheitsbilder und der Behandlungsmodalitäten verursacht ein Defizit an quantitativen Kenngrößen auf elektrophysiologischer Basis, die die Objektivität und die Effizienz der Diagnose und der Therapieevaluierung signifikant erhöhen können. Die Neurotherapie (bzw. EEG-Biofeedback) ist eine komplementäre Behandlung, die bei Erkrankungen, welche in Zusammenhang mit Regulationsproblemen des Zentralnervensystems stehen, angewandt wird. Obwohl sich die Applikationen dieser unkonventionellen Methode erweitern, wird sie nach wie vor stark diskutiert, da deren neuro- und psychophysiologischen Mechanismen wenig erforscht sind. Aus diesem Grund ist die Ermittlung von Kenngrößen als elektrophysiologische Korrelaten der ablaufenden Prozesse zur objektiven Einstellung und Therapievalidierung eines der Kernprobleme des Forschungsgebietes und auch der vorliegenden Arbeit.
Unter Berücksichtigung der aktuellen neurologischen Erkenntnisse und der durch Untersuchungen an Probanden, sowie an Epilepsie-Patienten gewonnenen Ergebnisse, wurden ein Konzept und eine Methodologie entwickelt, um die Objektivität in der Diagnose und Therapieevaluierung zu erhöhen. Die Methodologie basiert auf einem Neuroprofil, welches als ein signalanalytisches mehrdimensionales Modell eingeführt wurde. Es beschreibt einen strukturierten Satz quantifizierbarer Kenngrößen, die aus dem Elektroenzephalogramm (EEG), den ereignisbezogenen
Potentialen und dem Elektrokardiogramm extrahiert werden können. Als Komponenten des Neuroprofils wurden neuartige quantitative Kenngrößen (percentage epileptic pattern occurrence, contingent negative variation level difference measure, direct current recovery index, heart rate recovery ratio, hyperventilation heart rate index) definiert und die Methoden zu deren Berechnung algorithmisiert. Die Anwendbarkeit der Methodologie wurde beispielhaft für die Evaluierung von Neurotherapien an Epilepsie-Patienten demonstriert. Als Basis für eine zukünftige Automatisierung
wurden ein Softwarekonzept und entsprechende Tools (neuroprofile extraction module und die Datenbank ?NeuroBase?) entwickelt. Der Ansatz erfüllt auch die Anforderungen der patientenspezifischen Therapieplanung und kann auf andere Krankheitsbilder übertragen werden.
Durch die neu gewonnenen Erkenntnisse wurde die Synergetik als ein theoretischer Rahmen für die Analyse der Neurotherapie als komplexer Lernprozess vorgeschlagen. Es wurde die Hypothese aufgestellt, dass das Gleichspannungsniveau im EEG ein Ordnungsparameter des Gehirn ist, wobei das Gehirn als ein komplexes offenes System betrachtet wird. Für zukünftige Forschungen auf dem Gebiet wird empfohlen, die Wechselwirkungen zwischen den höheren kognitiven Funktionen und dem autonomen Nervensystem in diesem Kontext zu untersuchen
Models and Analysis of Vocal Emissions for Biomedical Applications
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
Investigation into the mechanisms of depressive illness
Functional and structural brain abnormalities have been reported in many imaging
studies of depressive illness. However, the mechanisms by which these
abnormalities give rise to symptoms remain unknown. The work described in this
thesis focuses on such mechanisms, particularly with regard to neural predictive error
signals. Recently, these signals have been reported to be present in many studies on
animals and healthy humans. The central hypothesis explored in this thesis is that
depressive illness comprises a disorder of associative learning. Chapter 2 reviews
the brain regions frequently reported as abnormal in imaging studies of depressive
illness, and the normal function of these particular brain regions. It is concluded that
such regions comprise the neural substrate for associative learning and emotion.
However, confidence in this conclusion is limited by considerable variability in the
human imaging literature. Therefore, chapter 3 describes a meta-analysis, which
tests the hypothesis that, consistent with the non-imaging literature, the ventromedial
prefrontal cortex is most active during emotional experience. The results of the
meta-analysis were clearly consistent with this hypothesis. Chapter 4 provides an
introduction to neural predictive error signals from the general perspective of
homeostatic physiological regulation. Both experimental evidence supporting the
error signals, and various formal mathematical theories describing the error signals,
are summarised. This provides the background to chapter 5, which describes an
original fMRI study which tested the hypothesis that patients with depressive illness
would exhibit abnormal predictive error signals in response to unexpected
motivationally significant stimuli. Evidence of such abnormality was found.
Chapter 6 describes a further original study using transcranial ultrasound and
diffusion tensor imaging of the brainstem, which investigated reports of a subtle
structural abnormality in depressed patients. If present, it might give rise to
abnormal error signals. However, no structural abnormality was found. Finally,
chapter 7 discusses the significance of these findings in the context of clinical
features of depressive illness and a wide range of treatments, ranging from
psychotherapy through antidepressants to physical treatments. A number of potential
future studies are identified, which could clarify understanding of depressive illness
Hope College Abstracts: 18th Annual Celebration of Undergraduate Research and Creative Activity
The 18th Annual Celebration of Undergraduate Research and Creative Activity was held on April 12, 2019 in the Richard and Helen DeVos Fieldhouse at Hope College and featured student-faculty collaborative research projects. This program is a record reflective of those projects between the 2018-2019 academic year
Cognitive processes in depression : the effects of content and presentation variables on organization and recall
The purpose of the present research was to investigate the relationship between depressed affect and the organization and recall of positively and negatively valenced affective information. Experiment 1 examined the clustering and recall performance of inpatient depressives, psychiatric patients, and normals as a function of positive and negative words presented either randomly or in blocked fashion. Experiment 2 examined the recall performance of the same three groups of subjects as a function of positively and negatively valenced words that the subjects either rated or generated in an incidental recall task
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